Lead Generative AI Engineer-Player Coach

About the job Lead Generative AI Engineer-Player Coach

Job Title:-Lead Generative AI Engineer / Player-Coach

Purpose: Own our Generative AI technical vision. You will rapidly prototype and lead a dedicated team of two engineers to launch our companys first intelligent search and content automation systems.

Role Summary

Were looking for a hands-on Gen AI pioneer who can architect, code, and mentor. This is a "player-coach" role where youll be building foundational systems while guiding your team. You will partner daily with product and engineering leadership to transform business goals into cutting-edge, shippable LLM-powered solutions.

Key Responsibilities

- Architect Build RAG Systems: Design, develop, and deploy sophisticated Retrieval-Augmented Generation (RAG) systems to power our next-generation search and discovery experience.
- Develop Fine-Tune LLMs: Lead the development of advanced generative models for nuanced tasks like automated content creation, summarization, and metadata enrichment.
- Own the Gen AI Stack: Select, provision, and optimize our stack, leveraging managed services like Azure OpenAI or AWS Bedrock, or self-hosting models on GPU infrastructure. You will establish best practices for repo structure, CI/CD, and model/prompt versioning.
- Implement LLMOps: Embed robust observability using tools like OpenTelemetry and Prometheus. This includes tracking standard metrics (latency, cost, accuracy) and specialized monitoring for hallucination, toxicity, and data drift.
- Lead Mentor: Hire, coach, and develop ML talent.



Set the standard for high-quality code, rigorous experimentation, and rapid iteration within the Gen AI domain.

Must-Have Skills

- Production LLM Experience: 5+ years in Python with demonstrable success in productionizing LLM applications using modern frameworks like DSPY, LangChain, LlamaIndex, or Hugging Face Transformers.
- RAG Expertise: Deep, practical knowledge of RAG architecture, including advanced prompt engineering, chunking strategies, and proficiency with vector databases (e.g., Pinecone, Weaviate, Milvus).
- Cloud Proficiency: Expertise with managed LLM services (Azure OpenAI Service or AWS Bedrock). Strong foundational cloud skills in either Azure or AWS for compute orchestration (AKS/EKS), serverless functions, and storage.
- MLOps Acumen: Solid experience with Docker, CI/CD pipelines (e.g., GitHub Actions, Argo), and model registries.
- Leadership Communication: Proven ability to lead small, highly technical teams and clearly communicate complex concepts to stakeholders.

Nice-to-Have Skills

- Experience with agentic workflows (e.g., AutoGen, CrewAI).
- Familiarity with multi-modal models (text, image, etc.).
- Knowledge of advanced LLM fine-tuning techniques (e.g., LoRA, QLoRA).
- Robust SQL skills (especially with ClickHouse) and a keen eye for inference cost optimization.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Lead Generative AI Engineer-Player Coach (Pune)
🏢 AcquireX
📍 Pune