Lead AI Engineer

What you'll do

• Lead AI Engineer in the Platforms and Products will…
• We are seeking a highly motivated Applied AI Engineer with a strong foundation in Machine Learning and a deep interest in Large Language Models (LLMs) and Generative AI.
• This role focuses on building, optimizing, and evaluating production-grade LLM systems, including Retrieval-Augmented Generation (RAG), fine-tuning workflows, and scalable inference pipelines.
• Design and implement LLM-powered applications using state-of-the-art transformer models.
• Build and optimize RAG pipelines using embeddings, chunking strategies, and vector search.
• Experiment with prompt engineering, structured outputs (JSON schemas/function calling), and tool-augmented LLMs (agents/workflows).
• Fine-tune models using techniques such as LoRA, PEFT, and instruction tuning.
• Develop and evaluate embedding models for similarity search and semantic retrieval.
• Conduct LLM evaluation using automated and human-in-the-loop techniques (offline + online).
• Optimize inference workflows for latency, GPU utilization, and cost efficiency (quantization, batching, caching).
• Build and maintain REST API Services (FastAPI etc.) to deploy LLM/RAG endpoints, integrate with product systems, and support scalable inference.
• Contribute to integration of AI systems into production software environments (CI/CD, monitoring, reliability).
• Research and prototype cutting-edge approaches in Generative AI and share learnings with the team.

What you’ll bring

• A master's or bachelor's degree in Computer Science or related field from a top university
• 4+ years' hands-on experience in Machine Learning (ML) with production LLM systems
• Good fundamentals of machine learning, deep learning and fine tuning models (LLM) including:
• Understanding of transformer architectures
• Prompt engineering expertise
• Embeddings and vector search
• Experienced in backend API design with FastAPI, async patterns, rate limiting
• Experience with vector DB including:
• Pinecone, Weaviate, or Chroma
• Embedding storage and similarity search
• Hybrid search implementations
• Strong programming expertise in Python is must including:
• Async programming (asyncio, async/await)
• Type hints and Pydantic
• SOLID principles and design patterns
• Experience in ML Ops to measure and track model performance including:
• MLFlow for model tracking
• Langfuse for LLM observability (strongly preferred)
• Model versioning and A/B testing
• Experience in working with NLP & computer vision
• Fluency in English
• Client-first mentality
• Intense work ethic
• Collaborative spirit and problem-solving approach