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.