Lead Generative AI Engineer-Player Coach
Lead Generative AI Engineer-Player Coach
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Job Description
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).
- Strong SQL skills (especially with ClickHouse) and a keen eye for inference cost optimization.
Employment Type
Full Time
