Lead Generative AI Engineer - Player Coach

Location : Viman Nagar, Pune

About AcquireX

Be part of the AcquireX team that unleashes the power of leading-edge technologies to help improve e-commerce processes in the e-commerce world.

Purpose

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

Role Summary

We're looking for a hands-on Gen AI pioneer who can architect, code, and mentor. This is a "player-coach" role where you'll 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.