Senior Data Scientist II - Agentic AI

Sr. Software Engineer - Ag & Trading .Net

IAD / SAD / LAD GCP Data Engineer

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

System engineer

Senior DevOps Engineer

Senior Data Scientist II

Consulting Principal AWS Cloud Engineer

Responsibilities: <ul><li>Develop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies).</li><li>Define agentic workflows and reasoning strategies for multi-step legal tasks.</li><li>Develop retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).</li><li>Analyze large-scale legal datasets to extract insights and improve model performance.</li><li>Establish best practices for model evaluation, validation, and benchmarking.</li><li>Translate experimental results into clear product recommendations and business impact.</li><li>Collaborate with product, legal experts, and engineers to align solutions with user needs.</li><li>Mentor team members and provide technical leadership in data science and AI.</li></ul> Requirements: <ul><li>Education: Master’s degree or above in a quantitative or technical field (Statistics, Computer Science, Mathematics, Data Science, etc.).</li><li>Strong experience in machine learning, NLP, and LLM-based modeling.</li><li>Strong experience designing and running experiments, including model evaluation and iteration.</li><li>Experience with generative AI techniques (e.g., prompt engineering, RAG).</li><li>Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases.</li><li>Experience designing agentic workflows and reasoning strategies, with hands-on experience applying agent frameworks (e.g., LangChain, LangGraph, AutoGen) in real-world use cases.</li><li>Proficiency in Python and data analysis tools.</li><li>Strong foundation in statistics, modeling, and large-scale text processing.</li></ul>

Responsibilities: <ul><li>Demonstrate accountability by ensuring timely and high-quality project delivery.</li><li>Be result driven, consistently achieving measurable outcomes and meeting goals.</li><li>Act as a team player, collaborating effectively with cross-functional teams.</li><li>Exhibit a can-do mentality, tackling challenges with a proactive and positive approach.</li><li>Apply software engineering patterns and principles to design, develop, test, integrate, maintain, troubleshoot and document complex and varied software solutions and incorporate security practices in newly developed and maintained applications.</li><li>Participate in peer code review sessions to review code, coach peers and ensure code quality.</li><li>Take the lead in the assigned agile team to adopt agile philosophies, facilitate agile ceremonies and identify continuous improvement opportunities.</li><li>Incorporate the companys engineering and development best practices within the full software development lifecycle including coding standards, code reviews, source control management, building processes, testing and security principles, to deliver high quality code rapidly.</li><li>Collaborate to lead demonstration and continuous feedback sessions to improve development and help drive the long term vision.</li><li>Participate in the engineering community by maintaining and sharing relevant technical approaches and modern skills.</li><li>Independently handle complex issues with minimal supervision, while escalating only the most complex issues to appropriate staff.</li><li>Other duties as assigned</li></ul> Requirements: <ul><li>Bachelor’s degree in a related field or equivalent experience</li><li>Confirmed experience automating pipelines for continuous integration, testing, delivery and security.</li><li>Confirmed experience architecting applications, databases, services or integrations.</li><li>Extensive experience with .NET Framework and .NET Core.</li><li>Familiarity with front-end technologies like HTML, CSS, JavaScript, and frameworks such as Angular, React, or Vue.js.</li><li>Experience with DevOps practices and tools such as Docker and CI/CD pipelines.</li><li>Knowledge of SQL Server and / or PostgreSQL for database design and management.</li><li>Hands-on experience with Kubernetes for container orchestration and management.</li><li>Experience with cloud platforms such as Azure, AWS, or GCP.</li><li>Minimum of 5 years of related work experience in software development using .NET technologies.</li></ul>

Responsibilities: Architecture &amp; Design:&nbsp; <ul><li>Design end-to-end data architectures that integrate real-time streaming (Kafka, GCP Pub/sub) and batch processing (Big Query OR Databricks).</li></ul> Pipeline Development:&nbsp; <ul><li>Build and maintain robust ETL/ELT pipelines flowing into BigQuery/Databricks, ensuring data is clean, modeled, and ready for consumption.</li></ul> Custom Integration:&nbsp; <ul><li>Develop custom APIs and microservices using Java/Python/C# to facilitate data movement between niche systems and the data lakehouse.</li></ul> Infrastructure Management:&nbsp; <ul><li>Deploy and manage data workloads on Google Kubernetes Engine (GKE), ensuring high availability and cost-efficiency.</li></ul> Technical Leadership:&nbsp; <ul><li>Mentor junior engineers, conduct code reviews, and establish DataOps best practices (CI/CD, automated testing, and monitoring).</li></ul> Data Reliability:&nbsp; <ul><li>Implement data quality frameworks to ensure accuracy and lineage across the hybrid environment.</li></ul> Requirements: <ul><li>Experience with Terraform &amp; Azure Dev Ops (ADO).</li><li>Knowledge of workflow orchestrators like Apache Airflow (Cloud Composer).</li><li>Experience implementing Data Governance (metadata management, data cataloging).</li><li>Data Processing &amp; Storage</li><li>BigQuery: Expert-level SQL and experience with partition/cluster optimization.</li><li>Databricks: Deep knowledge of the Lakehouse architecture, Delta Lake, and Spark (PySpark/Scala) Software Engineering &amp; APIs</li><li>Languages: Proficiency in Java/Python/C# for backend development and custom data connectors.</li><li>API Design: Proven ability to build and consume RESTful APIs for data ingestion Streaming &amp; Infrastructure</li><li>Kafka/GCP Pub-Sub: Experience building event-driven architectures and managing producers/consumers.</li><li>GKE: Proficiency in containerization (Docker) and orchestrating data workloads on Kubernetes.</li></ul>

Responsibilities: <ul><li>Ability to troubleshoot problems in a live environment and provide real time help with critical issues.</li><li>Write code that is easily understood and maintainable by other team members.</li><li>Keep up to date with developments in technologies we are using.</li><li>Ability to communicate and work effectively, supporting various business departments.</li><li>Ability to work in fast-paced interdisciplinary environment.</li></ul> Requirements <ul><li>BA/BS in Computer Science or equivalent practical experience.&nbsp;</li><li>At least 3+ years of post-university experience as a full-stack engineer.</li><li>Solid knowledge of programming fundamentals—algorithms, data structures, design patterns, and paradigms.</li><li>Strong knowledge of Python.</li><li>Solid knowledge SQL and relational databases.</li><li>Knowledge of Extract-Transform-Load (ETL) and big data analytics tools.</li><li>Experience working in a cloud environment (AWS, Azure, GCP).</li><li>Familiarity with popular Python data analytics frameworks.</li><li>Familiarity with Snowflake or other large analytics engines.</li><li>Experience with data warehousing and working in environments with large scale, complex analytics requirements.</li><li>Familiarity with DAG-based job scheduling tools.</li><li>Cloud-native container orchestration platforms and tools.</li><li>Experience working in the Financial Services industry.</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: Cloud Infrastructure Design and Deployment: <ul><li>Architect, design, and implement scalable, reliable, and secure solutions on GCP.</li><li>Deploy and manage GCP services in both development and production environments, ensuring seamless integration with existing infrastructure.</li><li>Implement and manage core services such as BigQuery, Datafusion, Cloud Composer (Airflow), Cloud Storage, Data Fusion, Compute Engine, App Engine, Cloud Functions and more.</li></ul> Infrastructure as Code (IaC) and Automation: <ul><li>Develop and maintain infrastructure as code using Terraform or CLI scripts to automate provisioning and configuration of GCP resources.</li><li>Establish and document best practices for IaC to ensure consistent and efficient deployments across environments.</li></ul> DevOps and CI/CD Pipeline Development: <ul><li>Create and manage DevOps pipelines for automated build, test, and release management, integrating with tools such as Jenkins, GitLab CI/CD, or equivalent.</li><li>Work with development and operations teams to optimize deployment workflows, manage application dependencies, and improve delivery speed.</li></ul> Security and IAM Management: <ul><li>Handle user and service account management in Google Cloud IAM.</li><li>Set up and manage Secrets Manager and Cloud Key Management for secure storage of credentials and sensitive information.</li><li>Implement network and data security best practices to ensure compliance and security of cloud resources.</li></ul> Performance Monitoring and Optimization: <ul><li>Monitoring &amp; Security: Set up observability tools like Prometheus, Grafana, and integrate security tools (e.g., SonarQube, Trivy).</li><li>Networking &amp; Storage: Configure DNS, networking, and persistent storage solutions in Kubernetes.</li><li>Set up monitoring and logging (e.g., Cloud Monitoring, Cloud Logging, Error Reporting) to ensure systems perform optimally.</li><li>Troubleshoot and resolve issues related to cloud services and infrastructure as they arise.</li></ul> Workflow Orchestration: <ul><li>Orchestrate complex workflows using Argo Workflow Engine.</li><li>Containerization: Work extensively with Docker for containerization and image management.</li><li>Optimization: Troubleshoot and optimize containerized applications for performance and security.</li></ul> Requirements:﻿ <ul><li>Bachelors or masters degree in computer science, Information Technology, or a similar field</li><li>Must have 10 + years of extensive experience in designing, implementing, and maintaining applications on GCP and OpenShift</li><li>Comprehensive expertise in GCP services such as GKE, Cloudrun, Functions, Cloud SQL, Firestore, Firebase, Apigee, GCP App Engine, Gemini Code Assist, Vertex AI, Spanner, Memorystore, Service Mesh, and Cloud Monitoring</li><li>Solid understanding of cloud security best practices and experience in implementing security controls in GCP</li><li>Thorough understanding of cloud architecture principles and best practices</li><li>Experience with automation and configuration management tools like Terraform and a sound understanding of DevOps principles</li><li>Proven leadership skills and the ability to mentor and guide a technical team</li></ul>

About the Role : <ul><li>As a data scientist on our team, you will work on new product development in a small team environment writing production code in both run-time and build-time environments.&nbsp;</li><li>You will help propose and build data-driven solutions for high-value customer problems by discovering, extracting, and modeling knowledge from large-scale natural language datasets including matter and contract repository, invoice/legal spend data and work management.&nbsp;</li><li>You will prototype new ideas, collaborating with other data scientists as well as product designers, data engineers, front-end developers, and a team of expert legal data annotators.</li><li>You will get the experience of working in a start-up culture with the large datasets and many other resources of an established company.</li><li>We are committed to advancing gender diversity within the technology sector and supporting greater representation of women in tech. To further this commitment,</li></ul> Responsibilities: <ul><li>Develop and implement LLM-based applications tailored for legal use-cases&nbsp;</li><li>Evaluate and help maintain our data assets and training/evaluation data sets&nbsp;</li><li>Design and build pipelines for preprocessing, annotating, and managing legal document datasets&nbsp;</li><li>Collaborate with legal experts to understand requirements and ensure ML-applications meet domain-specific needs&nbsp;</li><li>Conduct experiments and evaluate model performance to drive continuous improvements&nbsp;</li><li>Fine-tune and deploy large language models to enhance their performance on legal text processing tasks&nbsp;</li><li>Interface with other technical personnel or team members to finalize requirements.&nbsp;</li><li>Work closely with other development team members to understand complex product requirements and translate them into software designs.&nbsp;</li><li>Successfully implement development processes, coding best practices, and code reviews for production environments.</li></ul> Requirements:&nbsp; <ul><li>Able to work in person 2 days per week&nbsp;</li><li>Bilingual English and Spanish&nbsp;</li><li>Practical experience with large language models, prompt engineering, fine-tuning RAG-based applications and benchmarking, using frameworks such as LangChain&nbsp;</li><li>MS in Data Science, Computer Science, Statistics, Machine Learning, or relevant work experience&nbsp;</li><li>Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, Flair, BERT (ways to vectorize text data)&nbsp;</li><li>Formal training in machine learning: dimensionality reduction, clustering, embeddings, and sequence classification algorithms&nbsp;</li><li>Strong Python background&nbsp;</li><li>Knowledge of AWS, GCP, Azure, or other cloud platform&nbsp;</li><li>Understanding of data modeling principles and complex data models.&nbsp;</li><li>Proficiency with relational and NoSQL databases as well as vector stores (e.g., MySQL, Postgres, Solr, Elasticsearch/OpenSearch)</li><li>Knowledge of Spark-Scala, Ray or other distributed computing systems highly preferred&nbsp;</li><li>Knowledge of API development, containerization, and machine learning deployment highly preferred&nbsp;</li><li>Experience with ML Ops/AI Ops including experiment tracking tools like LangFuse and DVC&nbsp;</li><li>Experience with deep learning frameworks such as PyTorch, Tensorflow and Hugging Face Transformers Work in a way that works for you We promote a healthy work/life balance across the organization. </li><li>We offer an appealing working prospect for our people.&nbsp;</li><li>With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate. </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.</li></ul>

Responsibilities: <ul><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.&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.&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.&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.&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.&nbsp;</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.&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.</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.&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.&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.&nbsp;</li><li>Demonstrated ability to drive cultural change and advocate for cloud transformation Responsibilities&nbsp;</li><li>Develop and implement a comprehensive AWS cloud strategy, ensuring alignment with business and technology goals.&nbsp;</li></ul>

Responsibilities: <ul><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. 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. 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 • 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.</li></ul> Requirements: <ul><li>10+ years of experience in IT, with at least 5+ years in AWS cloud engineering and architecture. 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;&nbsp;</li></ul> Soft Skills&nbsp; <ul><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><li>Demonstrated ability to drive cultural change and advocate for cloud transformation Key&nbsp;</li></ul>

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12 containerization orchestration Jobs found

Senior Data Scientist II - Agentic AI

Sr. Software Engineer - Ag & Trading .Net

IAD / SAD / LAD GCP Data Engineer

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

System engineer

Senior DevOps Engineer

Senior Data Scientist II

Consulting Principal AWS Cloud Engineer

Consulting Principal AWS Cloud Engineer

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Senior Data Scientist II - Agentic AI

Sr. Software Engineer - Ag & Trading .Net

IAD / SAD / LAD GCP Data Engineer

Associate Data Engineer

Senior Machine Learning Engineer (Production & MLOps Focus)

System engineer

Senior DevOps Engineer

Senior Data Scientist II

Consulting Principal AWS Cloud Engineer

Consulting Principal AWS Cloud Engineer

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

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