ML & AI Engineer
Responsibilities
Develop, test and deploy ML, NLP, Generative BI, GenAI and AI-assisted automation solutions for enterprise environments.
Prepare data, perform feature engineering, train models and develop prompts, RAG pipelines and Agentic AI workflows.
Implement evaluation frameworks covering accuracy, groundedness, latency, robustness, safety and business acceptance criteria.
Integrate ML models and LLM/AI services with enterprise data platforms, semantic layers, APIs, BI and visualization tools.
Build reproducible MLOps pipelines for model packaging, deployment, monitoring, retraining and production lifecycle management.
Implement logging, explainability, human-in-the-loop review, security, privacy, governance and audit controls.
Support SIT/UAT, unit testing, defect resolution, production deployment, technical documentation, evaluation evidence and operational runbooks.
Requirements
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science or a related technical discipline.
6–9 years of experience in AI/ML, analytics or software engineering, with substantial hands-on experience delivering enterprise AI solutions.
Strong programming and ML expertise in Python, Spark, TensorFlow/PyTorch, scikit-learn, MLflow and modern LLM/Agent frameworks.
Hands-on experience with RAG, embeddings, vector search, prompt engineering, LLMs, Agentic AI, feature engineering and model/LLM evaluation.
Experience with APIs, containers, Kubernetes, cloud AI/ML services, Git, CI/CD, monitoring and production deployment practices.
Knowledge of responsible AI, model governance, data privacy, explainability and human-in-the-loop processes, preferably within banking, risk, AML, payments or customer analytics.
Strong ownership, analytical thinking, collaboration skills, with the ability to work independently, participate in peer reviews and deliver high-quality, traceable and production-ready solutions; relevant cloud AI/ML or Databricks certification is preferred.