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AI/ML Engineer

HCL Global Systems

Core Responsibilities

  • 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.
Responsibilities
  • 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.
Qualifications
  • 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 Designing and deploying production-grade ML systems.


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.
Nice to Have
  • Knowledge of data governance, model explainability, and responsible AI practices.
Vacancy posted 3 days ago
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