Lead AI Engineer

We are hiring a Lead AI Engineer to anchor and grow our AI practice, with strong hands-on expertise in generative AI and traditional machine learning. This role combines technical leadership, solution architecture, and delivery ownership across client and internal AI initiatives. The ideal candidate is deeply hands-on, production-focused, and capable of translating business problems into scalable AI solutions.
Responsibilities:
Lead design and development of AI solutions across generative AI (LLMs, RAG, embeddings, chatbots) and traditional ML (classification, regression, NLP, forecasting, etc. )
Own the end-to-end lifecycle: problem framing, model development, deployment, and monitoring.
Define best practices for model development, evaluation, and production readiness.
Mentor and guide junior ML engineers and data scientists.
Design and implement RAG-based systems, enterprise chatbots and copilots, and document intelligence solutions.
Build embedding pipelines and integrate vector databases.
Apply prompt engineering and fine-tuning where required.
Implement guardrails, evaluation metrics, and quality controls.
Develop and deploy ML models using standard frameworks.
Optimize model performance, scalability, and reliability.
Work with structured and unstructured data pipelines.
Ensure models are production-ready and measurable.
Implement CI/CD practices for ML workflows.
Containerize and deploy models using modern tooling.
Set up monitoring, retraining, and drift detection.
Collaborate with DevOps and cloud teams for scalable deployments.
Support pre-sales with solution design and effort estimation.
Drive POCs and accelerators in GenAI and ML.
Contribute to reusable frameworks and AI assets.
Stay updated with the evolving AI/LLM ecosystem.
Requirements:
Strong experience with modern GenAI design patterns, including advanced RAG (hybrid search, reranking), agentic orchestration (LangGraph or cloud-native ADK/Strands), and parameter-efficient fine-tuning (LoRA/QLoRA).
Experience with LLM evaluation, guardrails, and model observability in production environments.
Solid understanding of ML algorithms and deep learning.
Experience with frameworks such as TensorFlow, PyTorch, and Scikit-learn.
Experience architecting and deploying AI solutions on AWS, GCP, or Azure.
Knowledge of containerization (Docker) and orchestration (Kubernetes).
Familiarity with ML lifecycle management tools (e. g., MLflow, Airflow).
7-10 years in AI/ML engineering.
3+ years leading AI/ML initiatives or mentoring teams.
Proven track record of deploying ML or GenAI systems to production.

Experience
7-10 yrs