AI/ML Engineer
HCL Global Systems
Core Responsibilities
o Building scalable data pipelines and ML workflows.
o Managing model lifecycle in cloud environments.
- Agentic AI & MCP Integration: Implement agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for secure tool orchestration.
- Generative AI Development: Build LLM-based applications with RAG, structured output, and evaluation frameworks.
- AWS ML Engineering: Deploy models using SageMaker pipelines, ECS/ECR, Lambda; manage CI/CD and monitoring.
- Security & Identity: Integrate Okta/JWT token for API and service authentication; enforce token validation and claims.
- Governance : Deliver artifacts required by MDLC/MPLC (Model Documents, Data Dictionary, Monitoring Plan).
- Collaboration: Partner with PO, and business stakeholders to align solutions with objectives.
- Design, develop, and optimize complex data pipelines using machine learning engineering best practices to ensure scalability, efficiency, and reliability.
- Develop and implement robust MLOps pipeline to support the deployment, monitoring, and lifecycle management of AI/ML models in production environments.
- Integrate and maintain data and model pipelines, proactively diagnosing data quality issues and documenting assumptions.
- Collaborate closely with data scientists to validate model-ready datasets and ensure thorough, accurate feature documentation.
- Conduct exploratory data analysis and discovery on raw data sources, incorporating business context to support model development.
- Track data lineage and perform root cause analysis during early-stage exploration or issue resolution.
- Partner with internal stakeholders to understand business processes and translate them into scalable analytical solutions.
- Develop and maintain model monitoring scripts, investigate alerts, and coordinate timely resolutions.
- Act as a subject matter expert in machine learning engineering on cross-functional teams, contributing to high-impact initiatives.
- Stay current with advancements in AI/ML and evaluate their applicability to business challenges.
- Bachelor's degree in Computer Science, Engineering, or related field (Master's preferred).
- 6+ years of experience across Artificial Intelligence (AI) / Machine Learning (ML) engineering, data engineering, and MLOps implementation, including:
o Building scalable data pipelines and ML workflows.
o Managing model lifecycle in cloud environments.
- Proficient in Python and familiar with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
- Strong understanding and experience in AWS Machine Learning Stack including:
- AWS SageMaker
- AWS Glue
- AWS Bedrock
- AWS Data Pipelines
- AWS Lambda Functions
- Experience with Generative AI model development builing LLM based applications with RAG.
- Experience implementing agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for orchestration.
- Knowledge of React UI, GraphDB, and GenAI model performance evaluation
- Experience with CI/CD, containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes).
- Solid grasp of software engineering principles including testing, version control (e.g., Git), and security.
- Familiarity with the Machine Learning Development Lifecycle (MDLC) and best practices for reproducibility and scalability.
- Strong communication and collaboration skills, with experience working across technical and business teams.
- Ability to anticipate ambiguity and devise scalable solutions to address it.
- Knowledge of data governance, model explainability, and responsible AI practices.
Vacancy posted 4 days ago
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