AIML Engineer

Qualifications

• Bachelors degree in Computer Science Engineering or related field (Masters preferred).
• 6 years of experience across Artificial Intelligence (AI) / Machine Learning (ML) engineering data engineering and MLOps implementation including:
  • Designing and deploying production-grade ML systems.
  • Building scalable data pipelines and ML workflows.
  • Managing model lifecycle in cloud environments.
• Proficient in Python and familiar with ML frameworks such as TensorFlow PyTorch and Scikit-learn.
• Hands on experience with Databricks including:
  • Spark based data processing and feature engineering
  • Databricks ML/MLflow for experiment tracking and model management
  • Integrating Databricks with cloud native ML services
• Experience implementing Unity Catalog for centralized governance of data features and models including access controls lineage and auditability.
• 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.