Data Scientist _LLM Engineer_4+years

About The Opportunity

• Own design and implementation of end-to-end LLM solutions: data ingestion → retrieval (RAG) → fine-tuning → inference and monitoring for production workloads.
• Develop robust Python microservices to serve LLM inference, retrieval, and agentic workflows using LangChain/LangGraph or equivalent toolkits.
• Implement and optimise vector search pipelines (FAISS/Pinecone/Milvus), embedding generation, chunking strategies, and relevance tuning for sub-second retrieval.
• Perform parameter-efficient fine-tuning (LoRA/adapters) and evaluation workflows; manage model versioning and automated validation for quality and safety.
• Containerise and deploy models and services with Docker and Kubernetes; integrate with cloud infra (AWS/Azure/GCP) and CI/CD for repeatable delivery.
• Establish observability, alerting, and performance SLAs for LLM services; collaborate with cross-functional teams to define success metrics and iterate rapidly.
  

Skills & Qualifications

Must-Have

• 4+ years engineering experience with 2+ years working directly on LLM/Generative AI projects.
• Strong Python skills and hands-on experience with PyTorch and HuggingFace/transformers libraries.
• Practical experience building RAG pipelines, vector search (FAISS/Pinecone/Milvus), and embedding workflows.
• Experience with fine-tuning strategies (LoRA/adapters) and evaluation frameworks for model quality and safety.
• Familiarity with Docker, Kubernetes, cloud deployment (AWS/Azure/GCP), and Git-based CI/CD workflows.
• Solid understanding of prompt engineering, retrieval strategies, and production monitoring of ML services.
  

Preferred

• Experience with LangChain/LangGraph, agent frameworks, or building tool-calling pipelines.
• Exposure to MLOps platforms, model registry, autoscaling low-latency inference, and cost-optimisation techniques.
• Background in productionising LLMs for enterprise use-cases (knowledge bases, search, virtual assistants).
  

• Hybrid work model with flexible in-office collaboration and remote days; competitive market compensation.
• Opportunity to work on high-impact enterprise AI initiatives and shape production-grade GenAI patterns across customers.
• Learning-first culture: access to technical mentorship, experimentation environments, and conferences/learning stipend.