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.