Senior Software Engineer AI

Data & ML Architect

Senior Data Scientist III - Agentic AI

Senior Machine Learning Engineer (Production & MLOps Focus)

 IIAD / SAD / LAD Artificial Intelligence Engineer

Lead AI Engineer

Director, Data Science

Responsibilities: Design &amp; Build/Development &amp; Support <ul><li>Develop multi-agent workflow automation patterns using Agentic AI</li><li>Process redesign and mapping to agentic workflow patterns</li><li>Architect scalable micro-services that wrap LLM/RAG/Agent workflows (Python).</li><li>Implement robust prompt-engineering patterns, retrieval pipelines, and caching for AI Assistants and AI Agents</li><li>Profile inference latency, GPU/CPU utilization, and memory</li><li>Writes and maintains highly complex unit tests and integration tests, and performs debugging to maintain the quality and performance of the software/AI solutions</li><li>Lead bug-fix, security-patch, and performance-tuning sprints for live AI Assistants and AI Agents</li><li>Leads the establishment and maintenance of comprehensive documentation for agents, applications, deployment processes and system configurations.</li></ul> Operational Excellence&nbsp;<ul><li>Own on-call runbooks, SLOs, and incident reviews; embed observability</li><li>Leads and mentors technical support and troubleshooting for highly complex issues with deployed applications to ensure minimal downtime and fast resolution.&nbsp;</li></ul> Collaboration &amp; Leadership <ul><li>Leads cross functional team of engineers to gather requirements and deliver solutions that meet business needs.</li><li>Experience working within product and agile delivery frameworks including backlog management and cross-functionality delivery and full E2E engineering.&nbsp;</li><li>Experience with enterprise architecture, data governance, or security standards in the context of digital solution delivery as part of E2E engineering and solution architecture.&nbsp;</li></ul> Capability &amp; Technology Stack Exposure <ul><li>Experience with digital technologies in the commercial &amp; sales management capability (e.g. Salesforce, Service Cloud, CPQ, Salesloft, ChatGPT or similar platforms)&nbsp;</li></ul> Requirements: <ul><li>Minimum requirement of 4 years of relevant work experience. Typically reflects 5 years or more of relevant experience.&nbsp;</li><li>Typically reflects 4 years or more with 3+ years of cloud-native AI/ML or GenAI systems (Azure, AWS, or GCP) or 2+ years of software development.&nbsp;</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>

Responsibilities: <ul><li>Lead experimentation and model development for AI/ML solutions in legal products.</li><li>Design and evaluate 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>Define 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>Bachelor’s or Master’s degree in a relevant field (e.g., Computer Science, Data Science, Statistics, Mathematics).</li><li>Strong experience in machine learning, NLP, and LLM-based modeling.</li><li>Proven 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>Ability to define retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).</li><li>Able to analyze large-scale legal datasets to extract insights and improve model performance.</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: Agentic AI Development:&nbsp;&nbsp; <ul><li>Design, build, and deploy agentic AI systems using frameworks such as LangChain, Lang Graph, and related libraries.&nbsp;&nbsp;</li><li>Develop and deploy multi-agent systems capable of autonomous decision-making, reasoning, planning, and collaboration.&nbsp;</li></ul> RAG Pipelines:&nbsp; <ul><li>Implement and optimize retrieval-augmented generation (RAG) systems, ensuring agents can access and incorporate external knowledge sources for grounded, accurate responses.&nbsp;</li></ul> LLM Engineering:&nbsp; <ul><li>Fine-tune and prompt-engineer LLMs for task-specific reasoning, planning, and dynamic adaptation.&nbsp;</li><li>Work with LLM/SLM APIs, embeddings, and advanced generative AI techniques.&nbsp;</li></ul> Enterprise AI Platform:&nbsp; <ul><li>Lead the development of enterprise-grade AI platforms integrating LLMs, RAG, embeddings, and agentic AI protocols.&nbsp;</li><li>Implement and standardize Model Context Protocol (MCP) for consistent context management across models and agents.&nbsp;</li></ul> MLOps &amp; Observability:&nbsp; <ul><li>Establish and enforce best practices for MLOps, monitoring, and observability, ensuring scalable and maintainable AI solutions.&nbsp;</li><li>Applied AI Prototyping:&nbsp;</li></ul> Rapidly prototype, experiment, and iterate to improve AI agent capabilities.&nbsp; Collaboration &amp; Research:&nbsp; <ul><li>Participate in the full research cycle: literature review, data exploration, experimentation, and presentation of findings.&nbsp;</li><li>Collaborate effectively with other engineers, researchers, and data scientists.&nbsp;</li><li>Contribute to the documentation and standardization of technical code and practices.&nbsp;</li></ul>&nbsp;Requirements: <ul><li>Bachelor’s degree in Computer Science, Engineering, or a related quantitative field. Master’s or Ph.D. is a strong plus.&nbsp;</li><li>5+ years overall experience in software development, data science, or machine learning.&nbsp;</li><li>1+ year of hands-on experience developing AI applications with LLMs and systems such as retrieval-based methods, fine-tuning, or agent-based architectures.&nbsp;</li><li>1+ year of experience with frameworks like LangChain, LlamaIndex, OpenAI, or similar tools.&nbsp;</li></ul>

Responsibilities: <ul><li>Oversee the design, development, and deployment of AI models and intelligent agents.</li><li>Ensure engineering best practices in model development, testing, and deployment.</li><li>Collaborate with IT and data teams to ensure scalable and secure integration of AI solutions.</li><li>Work closely with business and technical stakeholders to understand requirements and deliver impactful AI solutions.</li><li>Communicate effectively with global teams, bridging technical and business perspectives.</li><li>Support change management and adoption of AI tools across business units.</li><li>Stay current with advancements in AI and apply them to solve business challenges.</li><li>Participate in efforts to improve enterprise adoption and proficiency in the use of AI tools.</li><li>Lead the design and development of solutions using Agentic AI&nbsp;</li></ul> Requirements: Basic Qualifications:<ul><li>10+ years of experience in software engineering, data science, AI, or a related field.</li><li>2+ years of experience in leadership role managing technical teams.</li><li>Proficiency in software development and statistical analysis.</li><li>Experience with Large Language Models and their applications.</li><li>Strong communication skills and the ability to present technical results to business stakeholders.</li><li>Experience with solution architecting and design.</li><li>Hands-on experience with cloud platforms such as Azure and/or AWS. </li><li>Proficiency in programming languages such as Python, R, or similar.</li><li>Bachelor’s or master’s in computer science, Mathematics, Statistics, or a related field.</li></ul> Preferred Qualifications:<ul><li>Experience working in a global enterprise or matrixed organization.</li><li>Familiarity with cloud platforms (Azure, AWS, GCP) and MLOps tools.</li><li>Experience with large language models and supporting workflows.</li><li>History of project leadership and development.</li><li>Experience in applications to sales, marketing, and market research&nbsp;</li></ul>

Responsibilities: Strategic Leadership:&nbsp; <ul><li>Define and execute the data science vision and roadmap aligned with business objectives and technological advancement opportunities.</li></ul> Team Management:&nbsp; <ul><li>Build, lead, and mentor a high-performing team of data scientists, fostering a culture of innovation, collaboration, and continuous learning.</li></ul> Advanced Research Direction:&nbsp; <ul><li>Direct cutting-edge research initiatives in NLP, LLMs, and other emerging AI technologies to maintain competitive advantage.</li></ul> Machine Learning and AI Solutions:&nbsp; <ul><li>Lead the development and implementation of machine learning algorithms and AI solutions to solve complex business problems.</li></ul> Data Collection and Preparation:&nbsp; <ul><li>Establish protocols for collecting, cleaning, and preprocessing large datasets, ensuring data quality and reliability.</li></ul> Data Visualization Strategy:&nbsp; <ul><li>Guide the creation of informative and compelling data visualizations to communicate results and insights to stakeholders effectively.</li></ul> Cross-functional Collaboration:&nbsp; <ul><li>Partner with executive leadership and cross-functional teams to identify strategic opportunities and address business challenges.</li></ul> Model Deployment and MLOps:&nbsp; <ul><li>Oversee the deployment of machine learning models into pro duction environments, ensuring scalability and reliability.</li></ul> Documentation Standards:&nbsp; <ul><li>Establish comprehensive documentation standards for projects, models, and code for knowledge sharing and reproducibility.</li></ul> Budget and Resource Management:&nbsp; <ul><li>Manage departmental budget, resource allocation, and infrastructure needs for data science operations.</li></ul> Ethical AI Governance:&nbsp; <ul><li>Develop and enforce ethical guidelines and best practices for AI development and deployment.</li></ul> Requirements: <ul><li>Bachelors, Masters or Ph.D. in Data Science, Computer Science, Statistics, or a related field; MBA or additional business education is a plus.</li><li>10+ years of progressive experience in data science, machine learning, or AI, with at least 8 years in leadership positions.</li><li>Demonstrated experience in managing and scaling data science teams of 15+ professionals.</li><li>Proven record of delivering high-impact AI and ML solutions that have driven significant business value.</li><li>Deep expertise with generative AI models and techniques (e.g., LLMs, GANs) for content generation and their practical applications.</li><li>Advanced knowledge of statistical analysis, machine learning algorithms, and data manipulation techniques at enterprise scale.</li><li>Experience in setting technical direction and implementing MLOps practices for model deployment and monitoring.</li><li>Strong business acumen with the ability to translate complex technical concepts into business value.</li><li>Excellent communication and leadership skills, with experience presenting to executive leadership.</li><li>Experience working in a global or multicultural environment.</li><li>Record of successful collaboration with product, engineering, and business teams.</li><li>Proficiency in multiple programming languages relevant to data science (Python, R, etc.) and big data technologies.</li></ul>

Responsibilities: <ul><li>Strategic Leadership: Define and execute the data science vision and roadmap aligned with business objectives and technological advancement opportunities.</li><li>Team Management: Build, lead, and mentor a high-performing team of data scientists, fostering a culture of innovation, collaboration, and continuous learning.</li><li>Advanced Research Direction: Direct cutting-edge research initiatives in NLP, LLMs, and other emerging AI technologies to maintain competitive advantage.</li><li>Machine Learning and AI Solutions: Lead the development and implementation of machine learning algorithms and AI solutions to solve complex business problems.</li><li>Data Collection and Preparation: Establish protocols for collecting, cleaning, and preprocessing large datasets, ensuring data quality and reliability.</li><li>Data Visualization Strategy: Guide the creation of informative and compelling data visualizations to communicate results and insights to stakeholders effectively.</li><li>Cross-functional Collaboration: Partner with executive leadership and cross-functional teams to identify strategic opportunities and address business challenges.</li><li>Model Deployment and MLOps: Oversee the deployment of machine learning models into pro duction environments, ensuring scalability and reliability.</li><li>Documentation Standards: Establish comprehensive documentation standards for projects, models, and code for knowledge sharing and reproducibility.</li><li>Budget and Resource Management: Manage departmental budget, resource allocation, and infrastructure needs for data science operations.</li><li>Ethical AI Governance: Develop and enforce ethical guidelines and best practices for AI development and deployment.</li></ul> Requirements: <ul><li>Bachelors, Masters or Ph.D. in Data Science, Computer Science, Statistics, or a related field; MBA or additional business education is a plus.</li><li>10+ years of progressive experience in data science, machine learning, or AI, with at least 8 years in leadership positions.</li><li>Demonstrated experience in managing and scaling data science teams of 15+ professionals.</li><li>Proven record of delivering high-impact AI and ML solutions that have driven significant business value.</li><li>Deep expertise with generative AI models and techniques (e.g., LLMs, GANs) for content generation and their practical applications.</li><li>Advanced knowledge of statistical analysis, machine learning algorithms, and data manipulation techniques at enterprise scale.</li><li>Experience in setting technical direction and implementing MLOps practices for model deployment and monitoring.</li><li>Strong business acumen with the ability to translate complex technical concepts into business value.</li><li>Excellent communication and leadership skills, with experience presenting to executive leadership.</li><li>Experience working in a global or multicultural environment.</li><li>Record of successful collaboration with product, engineering, and business teams.</li><li>Proficiency in multiple programming languages relevant to data science (Python, R, etc.) and big data technologies.</li></ul>

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8 mlops Jobs found

Senior Software Engineer AI

Data & ML Architect

Senior Data Scientist III - Agentic AI

Senior Machine Learning Engineer (Production & MLOps Focus)

IIAD / SAD / LAD Artificial Intelligence Engineer

Lead AI Engineer

Director, Data Science

Director, Data Science

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8 mlops Jobs found

Senior Software Engineer AI

Data & ML Architect

Senior Data Scientist III - Agentic AI

Senior Machine Learning Engineer (Production & MLOps Focus)

IIAD / SAD / LAD Artificial Intelligence Engineer

Lead AI Engineer

Director, Data Science

Director, Data Science

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

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