ML & AI Engineer

About the role

The ML & AI Engineer will design, develop, test and deploy machine‑learning, natural‑language processing and generative AI solutions for enterprise customers. You will work on data preparation, model training, prompt engineering and building end‑to‑end AI‑assisted automation workflows.

Key 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 create RAG pipelines, prompts 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 and operational runbooks.

Required profile

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Data Science or a related technical discipline.
• 6–9 years of experience in AI/ML, analytics or software engineering delivering enterprise AI solutions.
• Strong ownership, analytical thinking and collaboration skills with ability to work independently and deliver production‑ready solutions.

Required skills

• Python, Spark, TensorFlow, PyTorch, scikit‑learn, MLflow.
• LLM/Agent frameworks, RAG, embeddings, vector search, prompt engineering, Agentic AI.
• APIs, containers, Kubernetes, cloud AI/ML services, Git, CI/CD, monitoring and production deployment.
• Responsible AI concepts, model governance, data privacy, explainability and human‑in‑the‑loop processes.