AI Engineering Lead

Overview

AI Engineering Lead to lead end-to-end design, development, and deployment of production-ready AI solutions in Hyderabad.

What you'll do

• Lead end-to-end AI project delivery with governance and transparent communication of risks and decisions.
• Design and architect AI systems including RAG, agentic frameworks, and LLM-powered production solutions.
• Assess feasibility and select optimal approaches (prompting, RAG, fine-tuning, classical ML, or hybrids) based on evidence and business needs.
• Build evaluation frameworks using LLM-as-a-judge, custom metrics (recall@k, precision@k), and go/no-go gates.
• Execute data-driven experiments across prompts, retrievers, chunking, and models with documented iteration.
• Build and maintain inference infrastructure, CI/CD, deployment automation, and MLOps/LLMOps for the full lifecycle.
• Implement APIs, microservices, and orchestration layers focused on latency, cost, reliability, and security.

What you'll need

• 6+ years of hands-on experience building, deploying, and maintaining AI solutions in production.
• Expert proficiency in Python with software engineering practices (Git, code review, testing).
• Proven expertise designing and implementing RAG systems (chunking, embeddings, retrieval optimization, reranking, evaluation).
• Solid experience with cloud platforms (AWS, Azure, or GCP) including containerization and infrastructure management.
• Demonstrated track record with MLOps/LLMOps tools and frameworks (MLflow, Weights & Biases, or equivalent).
• Practical expertise in LLM versioning, model management, and experiment tracking.
• Excellent communication skills across engineering teams, technical stakeholders, and senior leadership.

Nice to have

• Experience with Databricks MLOps or similar enterprise ML platforms.
• Hands-on experience with LLM fine-tuning and transfer learning.
• Expertise building agentic GenAI systems and multi-step reasoning frameworks.
• Background in Infrastructure as Code (Terraform, CloudFormation, or equivalent).
• Experience implementing security, compliance, and observability for AI services.
• Active contributions to open-source AI/ML projects.
• Experience with advanced prompt engineering frameworks and tool-use optimization.
• Background in hiring, team building, and organizational development.
• Understanding of AI ethics, bias mitigation, and responsible AI practices.