Lead / Manager – AI Engineering

Job Description

A leading AI services organization is seeking a hands-on Lead / Manager – AI Engineering to drive the design, development, evaluation, and delivery of enterprise-grade Generative AI and Agentic AI solutions. The role focuses on LLMs, RAG, Azure AI, Snowflake, machine learning, AI evaluation, and cloud-native architectures, with strong emphasis on client delivery, innovation, and production readiness.

Location: Hyderabad, Telangana, India
Employment Type: Full-time
Work Arrangement: Remote

Key Responsibilities

• Translate business requirements into testable GenAI and Agentic AI solutions with clear outputs, measurable success criteria, scope boundaries, and risk considerations.
• Assess technical feasibility and determine the most appropriate approach, including prompting, RAG, fine-tuning, classical machine learning, or combinations of these methods.
• Design and implement prompting strategies, structured outputs, few-shot examples, tool-use prompts, and agent workflows, iterating based on evaluation results.
• Develop comprehensive evaluation strategies for GenAI systems and Agentic AI workflows, including metrics, ground-truth datasets, LLM-as-a-judge approaches, acceptance thresholds, and release gates.
• Lead structured experimentation across prompts, retrieval strategies, chunking methods, embeddings, models, and other system components to improve performance.
• Identify and diagnose model failures, including hallucinations, retrieval misses, instruction-following errors, formatting issues, and other reliability problems.
• Design scalable, secure Agentic AI architectures aligned with data engineering, MLOps, LLMOps, and cloud-native best practices.
• Prepare engineering-ready handoffs covering prompt packages, versioning, RAG configurations, tool schemas, evaluation harnesses, datasets, metrics, and go/no-go criteria.

Requirements

• 5–10 years of overall AI/ML experience, including at least 2–3 years of hands-on Generative AI experience.
• Strong background in applied machine learning, data science, LLMs, and Agentic AI engineering, with proven delivery and client-facing experience.
• Deep expertise in LLM evaluation, metric design, experimentation, and dataset curation.
• Proven experience in model selection and prompt engineering, including structured outputs and tool-use prompting.
• Strong proficiency in Python and major machine learning frameworks such as PyTorch, TensorFlow, and Scikit-learn.
• Hands-on experience with LLM fine-tuning, RAG, context engineering, Claude Code, OpenAI Codex, and Agentic AI workflows.
• Strong understanding of RAG architecture, including chunking, embeddings, retrieval strategies, reranking, and evaluation.
• Experience implementing an Agentic AI software development lifecycle (SDLC).
• Experience working with Generative AI on Azure, AWS, or Snowflake, including platforms such as Azure OpenAI, AWS Bedrock, or Snowflake Cortex.
• Experience with AI-assisted development tools such as Antigravity, Cursor, and VS Code is highly desirable.
• Proven ability to build end-to-end GenAI MVPs in Python, including RAG/agents and evaluation harnesses, and prepare them for production handoff.
• Excellent communication, stakeholder management, problem-solving, and strategic thinking skills.

Collaboration Requirements

• Partner with AI Engineering teams by providing clear implementation specifications, including prompts, tool schemas, evaluation harnesses, and acceptance thresholds.
• Mentor data scientists and analysts on GenAI evaluation methods, labelling processes, experimentation, and scientific rigor.
• Collaborate with Product and Software Engineering teams to integrate AI capabilities into platforms and user-facing applications.
• Work with DevOps and Platform Engineering teams on infrastructure, monitoring, environments, reliability, and deployment.
• Collaborate with Data Engineering teams to design and access upstream data pipelines and ensure data readiness for AI solutions.