Software Engineer – Ag & Trade

Enterprise Analytics – Machine Learning Ops/AI Projects (VP1)

Forward Deployed AI Scientist

Data & ML Architect

Senior Machine Learning Engineer

IAD / SAD Developer Ansible OpenShift

Senior Machine Learning Engineer (Production & MLOps Focus)

System engineer

Consulting Principal AWS Cloud Engineer

Applications Developer

Responsibilities: Software Development: <ul><li>Design, develop, test, and maintain scalable software applications using Java Spring Boot and JavaScript for backend and frontend development, respectively. Build clean, reusable, maintainable, and well-tested code following software engineering best practices.</li></ul> Full Stack Application Development: <ul><li>Develop end-to-end web and mobile applications using Java Spring Boot, React or React Native, backend APIs, database integrations, and modern UI frameworks to deliver intuitive user experiences.</li><li>Design and develop secure RESTful APIs, backend services, authentication mechanisms, and data access layers while ensuring application scalability and performance.</li></ul> AI Enable Engineering: <ul><li>Leverage AI-assisted development tools such as GitHub Copilot, Cursor, ChatGPT, Claude, Amazon Q, or similar tools to improve engineering productivity.</li><li>Experience integrating LLM APIs or AI-powered capabilities into applications is desirable.</li></ul> Collabration: <ul><li>Collaborate with Product Managers, Business Analysts, UX Designers, QA Engineers, Architects, and cross-functional engineering teams to understand business requirements and deliver scalable software solutions.</li><li>Participate in sprint planning, technical discussions, estimation, backlog refinement, and Agile ceremonies.</li></ul> Testing &amp; Quality: <ul><li>Develop unit tests, integration tests, and participate in peer code reviews to ensure software quality, maintainability, reliability, and performance.</li><li>Follow secure coding standards and engineering best practices.</li></ul> Cloud &amp; Devops: <ul><li>Contribute to CI/CD pipelines and cloud-native application deployments using technologies such as Docker, Kubernetes, Azure, AWS, GitHub Actions, or Azure DevOps.</li></ul> Continous Improvement: <ul><li>Identify opportunities to improve software architecture, engineering standards, automation, developer productivity, and application performance.</li><li>Adopt AI-assisted engineering workflows and modern software development practices.</li></ul> Technical Support: <ul><li>Troubleshoot production issues, perform root cause analysis, and resolve application issues while ensuring minimal business disruption.</li></ul> Requirements: <ul><li>Bachelors degree in Computer Science, Engineering, Information Technology, or a related field (or equivalent practical experience).</li><li>3–5 years of professional software development experience.</li><li>Minimum 3+ years of hands-on full-stack development experience.</li><li>Experience developing production-grade web or mobile applications (not academic or personal projects).</li><li>Strong proficiency in Java Spring Boot and JavaScript.</li><li>Experience developing applications using React or React Native.</li><li>Experience developing backend services using Node.js or equivalent backend technologies.</li><li>Strong understanding of REST API development.</li><li>Experience with relational databases such as PostgreSQL, MySQL, SQL Server, or equivalent.</li><li>Strong knowledge of Git and modern software engineering practices.</li><li>Experience working in Agile/Scrum teams.</li><li>Strong understanding of object-oriented programming, software design principles, and data structures.</li></ul>

Responsibilities: <ul><li>Develop and maintain automation scripts using Linux shell scripting, Python, or other relevant tools.</li><li>Develop and maintain the entire DevOps pipelines.</li><li>Ensure seamless deployment and integration between cloud/prem environments (AWS).</li><li>Integrate AI models into production environments using containerized platforms such as OpenShift.</li><li>Implement and maintain network security protocols to safeguard AI systems and data pipelines.</li><li>Collaborate with cross-functional teams to understand AI workflows and translate them into robust engineering solutions.</li><li>Monitor and optimize system performance, reliability, and scalability.</li><li>Support CI/CD processes and infrastructure for AI model deployment and updates.</li></ul> Requirements: <ul><li>Bachelor’s degree in Computer Science, Engineering, or a related field.</li><li>Bash and Unix/Linux command-line toolkit is a must-have.</li><li>Deep technical background, with hands-on experience in supporting CI/CD pipelines (Bitbucket, Jenkins, Artifactory, Sonar, Veracode for SAST, JIRA workflow, and Confluence).</li><li>Hands-on experience with OpenShift, Docker, Kubernetes.</li><li>Knowledge of cloud platforms (e.g. AWS) is a must-have.</li><li>Exposure to data and network security and compliance in AI systems.</li><li>Knowledge of API integration and microservices architecture.</li><li>Proficiency in Python used both for automation and ML-related tasks</li><li>Knowledge of Workflow Orchestrator, such as Ctrl-M</li><li>Good knowledge of Logging and Monitoring tools, such as Splunk and Geneos.</li><li>Experience with Observability framework, such as Langfuse, Elastic Stack, Grafana, Open Telemetry.</li><li>Understanding of Generative AI (e.g. prompt engineering, RAG pipelines) and Agentic AI concepts.</li></ul>

Role Description: <ul><li>The Strategy team is looking for a hands-on AI-enabled strategy and market intelligence professional to materially accelerate the way we conduct deep research, market intelligence, analytical modeling, and GTM analysis by applying high impact AI workflows and translating them into sharper strategic decisions.</li><li>The individual will partner with business, product, commercial, and tech teams to translate ambiguous questions into actionable insights, while developing and scaling AI-enabled solutions that improve strategic decision-making and drive measurable business impact.&nbsp;</li><li>The individual is expected to work in close partnership with our client IT team, to leverage existing AI &amp; data capabilities on Databricks &amp; AWS (where applicable) while developing solutions.</li></ul> Responsibilities: Strategic problem solving and driving cross-functional execution&nbsp; <ul><li>Translate AI-assisted analyses into practical executive decisions by applying strong business judgment, rigorous validation protocols, and a sharp “so-what” orientation</li><li>Collaborate with and influence senior stakeholders across strategy, sales, technology teams, and P&amp;L owners</li><li>Good first-principles and problem-solving capabilities with the ability to build and test strategic hypotheses Identifying meaningful opportunities to leverage AI and deploying scaled AI solutions at pace</li></ul> Identify, assess, and prioritize high-value AI and automation opportunities by translating business challenges into scalable, practical solutions&nbsp; <ul><li>Design proprietary AI-enabled workflows, agents, and accelerators for recurring strategy activities (competitor monitoring, earnings call tracking, tariff and regulatory intelligence, voice-of-customer mining etc.)</li><li>Build reusable templates, workflows, and modules to drive adoption and embed AI capabilities into strategy processes</li></ul> Executing rapid AI-augmented research and competitive intelligence&nbsp; <ul><li>Lead AI-augmented research using tools like Claude, OpenAI, Perplexity, AlphaSense, CapIQ etc. to deliver source-triangulated competitive and industry insights</li><li>Synthesize structured datasets (S&amp;P Capital IQ, Bloomberg, IDC, Gartner) and unstructured sources (10-Ks, analyst reports, expert interviews) – to generate and validate AI-assisted outputs</li></ul> Support strategy team in day-to-day execution by leveraging AI-led tools <ul><li>Lead AI-enabled market sizing, opportunity mapping, and TAM/SAM/PAM modelling</li><li>Accelerate financial modelling – scenario and sensitivity analyses, valuation, NPV/IRR, segment P&amp;Ls, pricing</li><li>Support M&amp;A pipeline by AI-screening targets and generating investment theses</li></ul> AI enablement, coaching and continuous innovation <ul><li>Coach strategy team members on prompting techniques, research workflows, and AI-fluent execution</li><li>Scale AI adoption across Corporate Strategy through hands-on mentorship of beginner users</li><li>Stay current on agentic stacks, multimodal models, and deep research agents</li></ul> Requirements: Skillset and capabilities AI fluency and hands-on deployment orientation&nbsp; <ul><li>Working knowledge of GenAI patterns – LLM capabilities/limitations, prompt design, etc.</li><li>Proven ability to convert business problems into reusable AI workflows, tools, or accelerators</li><li>Builder mindset with experience prototyping AI solutions using frontier tools &amp; low-code platforms</li></ul> Analytics, modelling, and research excellence <ul><li>Strong modelling skills across TAM/SAM/PAM, scenarios, sensitivities, NPV/IRR, valuation etc.</li><li>Ability to synthesize structured datasets with unstructured sources</li><li>Research discipline – source triangulation and rigorous validation of AI outputs</li></ul> Strategic problem solving, communication, executive presence, and influence&nbsp; <ul><li>Sharp "so-what" orientation to translate AI outputs into practical executive decisions</li><li>Crisp written and verbal communication, with ability to frame board-ready narratives</li><li>Skill in explaining AI workflows and implications to both technical and non-technical audiences</li><li>Stakeholder management across strategy, commercial, data, and technology teams</li></ul> Ownership, and execution <ul><li>Independently own workstreams end-to-end – from framing through deployment</li><li>High bar for quality; never publishes unchecked AI outputs or unvalidated analysis</li></ul> Coaching and capability building&nbsp; <ul><li>Patience and ability to coach colleagues on prompting, research workflows, and source validation</li><li>Track record of building reusable playbooks – templates and training modules that scale adoption</li></ul> Passion, curiosity, and learning agility <ul><li>Deep passion for AI, tech, and industrial innovation, with curiosity to stay current on frontier tools</li><li>Entrepreneurial ownership mindset and drive to build a new AI-enabled strategy capability</li></ul> Qualifications:&nbsp; <ul><li>BE / B-Tech in Computer Science Engineering, Data Science or a related analytical field required; MBA, MS, M-Tech or equivalent advanced degree strongly preferred.</li><li>3–4+ years of experience in AI transformation, analytics, or AI-enabled business building. Prior Forward Deployed Engineer / Solutions Architect / Senior Data Scientist background preferred.</li><li>Experience in a top-tier strategy consulting firm, AI-native consulting team, analytics firm or corporate / product strategy team role strongly preferred.</li><li>Demonstrated experience building or deploying 0–1 AI solutions (not just experimenting), GenAI, analytics, automation, or workflow solutions in a business environment required.</li><li>Proficiency in Excel and PowerPoint required; Python or equivalent scripting capability strongly preferred. Experience with DevOps, CI/CD pipelines, and infra as code (e.g., Jenkins, Docker, Kubernetes) required.</li><li>Proficient in implementing AI platforms like AWS Bedrock, LangGraph, Crew AI, Glean etc.</li><li>Knowledge of enterprise data environments (esp. SAP, Databricks) or cloud platforms (esp. AWS) is a plus. Experience integrating AI systems with enterprise data platforms required.</li><li>Experience with LLM-powered solutions, RAG systems, agentic workflows, AI research tools, or AI-enabled knowledge manage</li></ul>

Responsibilities: Strategic Leadership: <ul><li>Set the vision and direction for data and AI architecture for project and client.</li><li>Influence enterprise data and AI strategies, driving adoption of best practices in data, ML, and AI architecture.</li><li>Mentor and develop junior architects and engineers, fostering a culture of technical excellence and innovation.</li></ul> Architectural Excellence: <ul><li>Architect and design enterprise-scale data platforms and ML/AI solutions for scalability, reliability, and performance.</li><li>Define and enforce data and ML architecture standards, patterns, and governance frameworks for data pipelines, ML workflows, and analytics platforms.</li><li>Lead solution design for integrating data from diverse sources (streaming, batch, cloud, on-premises) using Azure, AWS, and GCP services.</li></ul> AI &amp; ML Leadership: <ul><li>Architect advanced Data Science, ML and AI solutions, including NLP, RAG LLM and Agentic AI systems.</li><li>Oversee the implementation of MLOps frameworks, ensuring robust deployment, monitoring, and lifecycle management of ML models.</li><li>Evaluate and recommend emerging AI/ML technologies and platforms, driving innovation in intelligent data products.</li><li>Provide Ethical AI leadership to ensure intelligent solutions are implemented when required and in safe methods.</li></ul> Technical Leadership: <ul><li>Oversee data modelling (e.g. medallion architecture), data governance, and quality frameworks. Implementing technical reviews and driving high standards.</li><li>Collaborate with engineering and DevOps teams to establish CI/CD and MLOps architectures using Azure DevOps, Kubernetes, and Terraform.</li><li>Guide the selection and implementation of visualization and analytics tools (Power BI, Tableau, PowerApps).</li></ul> Stakeholder Engagement: <ul><li>Engage with senior stakeholders to translate complex business requirements into technical solutions and strategic roadmaps.</li><li>Ensure all solutions meet security, compliance, and performance standards, and align with enterprise architecture principles.</li></ul> Innovation &amp; Emerging Technologies: <ul><li>Apply modern data and AI architecture patterns (Cloud Native, Steaming/batch ingestion, Agentic AI Design) and leverage platforms such as AI/ML, Big Data, and Data Lakehouse.</li></ul> Requirements: <ul><li>Minimum 8 years’ experience client facing roles specializing in Data and Architecture roles.</li><li>Experience in both Agile and Waterfall project environments.</li><li>Strong stakeholder management and communication skills.</li><li>Security and compliance awareness.</li><li>Industry certifications (e.g., AWS Solution Architect, Azure Data Engineer, GCP Data Engineer).</li><li>Experience mentoring or managing technical teams.</li></ul>

Role Description: <ul><li>Working closely with the Data Scientists and be responsible for implementing large language models (LLMs) based applications, working with proprietary and open-source Models as well as popular frameworks such as Lang Chain to ensure seamless integration and deployment.</li></ul> Responsibilities: <ul><li>Building standalone applications that interact with LLM models.</li><li>Building RAG-based applications.</li><li>Understanding of Vector DBs like Solr for LLM based applications</li><li>Integrate models with existing systems and APIs.</li><li>Implement production quality ETL jobs</li><li>Preprocess and manage data for training and deployment.</li><li>Collaborate with cross-functional teams to define, design, and ship new features.</li><li>Write clean, maintainable, and efficient code.</li><li>Document development processes, code, and APIs.</li></ul> Requirements: <ul><li>Degree in Computer Science, Data Science, or related field.• Proven experience in building customer facing ML based APIs</li><li>Mexico City–based (or nearby), available to work onsite 2 days per week at Glorietadel Ahuehuete, Reforma.</li><li>Experience in developing applications that are scalable to handle TBs of data</li><li>Strong knowledge of API integration (RESTful, GraphQL).</li><li>Experience with data preprocessing, SQL, and NoSQL databases as well as vector stores (e.g., Postgres, MySQL, Solr, Elasticsearch/OpenSearch, etc.).</li><li>Familiarity with deployment tools (Docker, Kubernetes).</li><li>Experience with DevOps tools like Jenkins, Terraform, Cloud Formation templates is highly preferred.</li><li>Excellent problem-solving and communication skills.</li><li>Experience with Spark/Hadoop, EMR or any other Big Data technology would be a plus</li><li>Experience working on agents is considered a good-to-have</li><li>Familiarity with agents, even through courses Work in a way that works for you We promote a healthy work/life balance across the organization..</li></ul> Other Information: These are some of the benefits we are delighted to offer: <ul><li>Private Medical/Dental Plan.</li><li>Savings Fund.</li><li>Life Insurance.</li><li>Meal/Grocery Voucher.</li><li>We offer an appealing working prospect for our people.</li><li>With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.</li><li>Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive Working for you.</li><li>We know that your well-being and happiness are key to a long and successful career.</li></ul>

Responsibilities: <ul><li>Perform systems analysis and design for applications deployed using Ansible and OpenShift.</li><li>Design and develop moderate to complex applications in alignment with business and technical requirements.</li><li>Create, maintain, and ensure accuracy of comprehensive application documentation.</li><li>Define, build, and deliver application and system integration builds.</li><li>Monitor, evaluate, and apply emerging technology trends to improve system architecture and development practices.</li></ul> Requirements: <ul><li>5+ years of experience</li><li>Bachelor’s Degree or International equivalent&nbsp;</li><li>Extensive experience with core Java concepts and Java EE.</li><li>Extensive experience with Spring and Spring Boot.</li><li>Experience with CI/CD code automation, Ansible &amp; DevOps</li><li>Experience with OpenShift</li><li>Good understanding of software architecture and design principles.</li><li>Understanding of data structures, programming logic, and design</li><li>Understanding of application design patterns</li><li>Excellent written &amp; verbal communication skills</li><li>Excellent attention to detail</li></ul>

Responsibilities: <ul><li>Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.&nbsp;</li><li>Build and scale ML pipelines using Vertex AI Pipelines, Kubeflow, Airflow, and manage infra-as-code with Terraform/Helm.&nbsp;</li><li>Implement automated retraining, drift detection, and re-deployment of ML models.&nbsp;</li><li>Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.&nbsp;</li><li>Implement model monitoring, observability, and alerting across accuracy, latency, and cost.&nbsp;</li><li>Integrate and manage feature stores, knowledge graphs, and vector databases for advanced ML/RAG use cases.&nbsp;</li><li>Ensure pipelines are secure, compliant, and cost-optimized.&nbsp;&nbsp;</li><li>Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance .&nbsp;</li><li>Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.&nbsp;</li><li>Stay current with the latest ML research and apply new tools pragmatically to production systems.&nbsp;</li><li>Collaborate with product managers, DS, and engineers to translate business problems into reliable ML systems.&nbsp;</li></ul> Requirements: <ul><li>Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).&nbsp;</li><li>5+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.&nbsp;</li><li>Strong foundations in data structures, algorithms, and distributed systems.&nbsp;</li><li>Proficient in Python (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.&nbsp;</li><li>Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).&nbsp;</li><li>Experience with containerization (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).&nbsp;</li><li>Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform/Helm), and configuration management.&nbsp;</li><li>Experience with big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).&nbsp;</li><li>Practical exposure to model observability tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)&nbsp;</li><li>Strong understanding of statistical methods, ML algorithms, and deep learning architectures.&nbsp;</li></ul>

Responsibilities: <ul><li>Provide application L1/L2 support and system checks (CPU, disk usage, logs, services)</li><li>Troubleshoot and resolve system and application issues, and provide on-site technical support to ensure timely resolution and minimal disruption to business operations</li><li>Monitor application health and system alerts, escalating issues as needed</li><li>Log incidents and service requests in the ticketing system, ensuring timely resolution</li><li>Coordinate with L3 teams and vendors for issue escalation and follow-up</li><li>Perform routine maintenance tasks, such as application updates, patching, and backups</li><li>Maintain clear documentation of issues, fixes, and procedures</li><li>Participate in system testing, deployment, and post-deployment support activities</li><li>Assist in user access management and configuration </li><li>Requirements:</li><li>Diploma/Degree in Electrical Engineering, Computer Science, Communications Engineering or equivalent</li><li>Minimum 2 years in application maintenance, support or server infrastructure setup</li><li>Experience participating in Kubernetes cluster projects, with proficiency in using relevant commands to check application logs</li><li>Experience with at least two of the following Virtualization Technology/Platforms: VMware, Xen, KVM, OpenStack, Cloud Stack, Azure Stack</li><li>Experience with cloud orchestration, configuration management and auto-scaling techniques</li><li>Thorough knowledge of networking, routers, switches, and WiFi setup</li><li>Hands on experience in deploying ceph using different file system such as object storage, rbd, cephfs storage</li><li>Good knowledge of storage technologies, such as NAS, SAN, SDS</li><li>Experience with automation tools such as Salt/Chef/Puppet/Ansible</li><li>Experience with load balancing setup for TCP/IP, HTTP, DNS, routings.</li><li>Experience in using software version control</li><li>Knowledge of IT security standards, for instance from CIS or IM8.</li><li>Experience in IT security policy management and enforcing IT security</li><li>Knowledge of computer hardware components and ability to restore faulty servers to working condition.</li><li>Experience in coding or scripting languages such as bash, windows batch script, powershell, perl, python, etc.&nbsp;</li></ul>

Responsibilities: <ul><li>Demonstrated ability to drive cultural change and advocate for cloud transformation Key Responsibilities Cloud Strategy &amp; Leadership&nbsp;</li><li>Develop and implement a comprehensive AWS cloud strategy, ensuring alignment with business and technology goals.&nbsp;</li><li>Provide technical leadership and advocacy, championing cloud-native best practices and innovation.&nbsp;</li><li>Lead and drive architectural decisions, ensuring scalability, security, and operational efficiency.</li><li>Cloud Architecture &amp; Design&nbsp;</li><li>Architect secure, scalable, and highly available AWS cloud environments.&nbsp;</li><li>Develop and maintain enterprise-wide cloud blueprints, patterns, and frameworks to drive consistency.&nbsp;</li><li>Research and integrate emerging cloud technologies to foster continuous innovation. Infrastructure as Code &amp; Automation&nbsp;</li><li>Lead adoption and implementation of Infrastructure as Code (IaC) using Terraform, AWS CloudFormation, and Helm.&nbsp;</li><li>Design and optimize CI/CD pipelines to streamline cloud deployments and operations.&nbsp;</li><li>Implement self-healing and automated failover mechanisms to enhance resilience and reliability.</li><li>Kubernetes &amp; Cloud-Native Technologies&nbsp;</li><li>Manage and optimize Kubernetes clusters (EKS, Helm, IAM) for scalability and security.&nbsp;</li><li>Define and enforce best practices for containerized applications and orchestration.&nbsp;</li><li>Improve Kubernetes networking, ingress, and observability solutions to enhance performance. Security &amp; Compliance&nbsp;</li><li>Establish and enforce security best practices for AWS environments, including IAM, WAF, Secret Manager, and encryption.&nbsp;</li><li>Implement zero-trust security models and secure multi-account AWS environments.&nbsp;</li><li>Conduct security audits, incident response, and ensure compliance with industry standards. Monitoring, Logging &amp; Reliability Engineering.</li><li>Deploy and manage monitoring tools (DataDog, Splunk, CloudWatch, Coralogix) to ensure visibility and observability.&nbsp;</li><li>Implement SRE principles to enhance system uptime, reliability, and efficiency.&nbsp;</li><li>Conduct post-mortem analysis and drive continuous improvements in cloud performance. Collaboration &amp; Thought Leadership&nbsp;</li><li>Work closely with software developers, system engineers, and security teams to design and implement cloud solutions.&nbsp;</li><li>Provide mentorship, training, and technical leadership to engineering teams.&nbsp;</li></ul> Requirements: <ul><li>10+ years of experience in IT, with at least 5+ years in AWS cloud engineering and architecture.</li><li>Deep understanding of AWS services, including EC2, EKS, RDS, S3, Lambda, VPC, IAM, Transit Gateway, and CloudFormation.&nbsp;</li><li>Expertise in Kubernetes (EKS, Helm, Daemon Sets, StatefulSets, Karpenter, external-dns, aws-cni).&nbsp;</li><li>Strong experience with Terraform, CloudFormation, and automation scripting (Bash, Python, PowerShell).&nbsp;</li><li>Hands-on experience managing CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps).&nbsp;</li><li>Proficiency in monitoring and logging tools (DataDog, Splunk, Coralogix, CloudWatch).&nbsp;</li><li>Strong networking and security knowledge (DNS, VPC, NAT, Security Groups, IAM).&nbsp;</li><li>Experience designing and developing scalable, production-grade software tools and automation scripts using Python. Preferred Certifications&nbsp;</li><li>AWS Certified Solutions Architect – Professional (or equivalent).&nbsp;</li><li>Additional certifications in Kubernetes (CKA), AWS Security, or DevOps are a plus. Leadership &amp; Soft Skills&nbsp;</li><li>Proven experience leading cloud architecture and strategy initiatives.&nbsp;</li><li>Ability to mentor and guide technical teams in a fast-paced, agile environment.&nbsp;</li><li>Strong problem-solving and troubleshooting skills in complex cloud environments.</li><li>Excellent communication skills with the ability to translate technical concepts for engineering, business, and executive stakeholders.&nbsp;</li></ul>

Responsibilities:Assists with Information Systems projects.Assists in system analysis and design.Designs and develops low to moderately complex applications.Generates application documentation.Supports integration builds.Assists with maintenance and support.Requirements:Azure Cloud, .NET core, Java, Python, Angular JS, SQL Server, CI/CD Technologies, Kubernetes.Secondary Skills:SAFe Framework, Scrum MastersBachelor’s Degree or International equivalentBachelor's Degree or International equivalent in Computer Science, Information Systems,Mathematics, Statistics or related field - Preferred

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11 kubernetes Jobs found

Software Engineer – Ag & Trade

Enterprise Analytics – Machine Learning Ops/AI Projects (VP1)

Forward Deployed AI Scientist

Data & ML Architect

Senior Machine Learning Engineer

IAD / SAD Developer Ansible OpenShift

Senior Machine Learning Engineer (Production & MLOps Focus)

System engineer

Consulting Principal AWS Cloud Engineer

Applications Developer

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11 kubernetes Jobs found

Software Engineer – Ag & Trade

Enterprise Analytics – Machine Learning Ops/AI Projects (VP1)

Forward Deployed AI Scientist

Data & ML Architect

Senior Machine Learning Engineer

IAD / SAD Developer Ansible OpenShift

Senior Machine Learning Engineer (Production & MLOps Focus)

System engineer

Consulting Principal AWS Cloud Engineer

Applications Developer

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WhiteCrow is on a mission to connect companies with the best talent across the globe.