Senior Technical Lead

10+ years (min. 2 years in GenAI / LLM systems) We are seeking a Senior AI/ML Engineer to design, develop, and deploy production-grade AI/ML solutions within GSK’s Digital & Tech organization. This role focuses on building Generative AI applications, multi-agent systems, and advanced retrieval pipelines that drive measurable business impact. You will work at the intersection of cutting-edge AI research and enterprise software engineering, collaborating with data scientists, platform engineers, and domain experts across R&D, supply chain, and commercial functions.

Key Responsibilities

Generative AI & LLM Development Design, develop, and deploy Generative AI applications using LLMs (GPT-4, Claude, Gemini, open-source models) for enterprise use cases Build and orchestrate multi-agent systems using frameworks like LangGraph, LangChain, CrewAI, or AutoGen with function calling and tool use Implement Retrieval-Augmented Generation (RAG), Graph RAG, and hybrid retrieval pipelines using vector databases (Pinecone, Weaviate, Chroma, pgvector) Apply prompt engineering, chain-of-thought reasoning, and context engineering techniques to optimize model outputs Fine-tune LLMs and embedding models for domain-specific tasks using LoRA, QLoRA, or full fine-tuning approaches Implement guardrails, content filtering, and safety mechanisms for responsible AI deployment ML Engineering & MLOps Build end-to-end ML pipelines – data ingestion, feature engineering, model training, evaluation, and deployment Implement LLMOps practices: model versioning, A/B testing, prompt management, evaluation frameworks (LLM-as-judge, RAGAS, custom metrics) Deploy and manage LLM inference using frameworks such as vLLM, TensorRT-LLM, or DeepSpeed for latency and cost optimization Monitor model performance, detect drift, and implement continuous improvement loops Build observability for AI systems using LangSmith, Langfuse, or custom tracing solutions Architecture & Cloud Architect scalable AI solutions on AWS (Bedrock, SageMaker, Lambda) or Azure (OpenAI Service, ML Studio) Containerize AI applications with Docker and deploy via Kubernetes, ECS, or serverless patterns Design event-driven and API-first architectures for AI service integration with enterprise systems Implement CI/CD pipelines for ML models and AI applications Collaboration & Leadership Collaborate with data scientists, domain experts, and product owners to translate business problems into AI solutions Conduct code reviews, architectural design reviews, and contribute to engineering standards Mentor junior AI/ML engineers; lead technical knowledge-sharing sessions Evaluate and recommend emerging AI technologies, frameworks, and approaches Present AI solutions and results to technical and non-technical stakeholders Skill Requirements Skill Area Required Proficiency / Technologies GenAI & LLMs LangChain, LangGraph, OpenAI API, Claude API, Hugging Face Transformers, prompt engineering, multi-agent orchestration ML Frameworks PyTorch, TensorFlow, Scikit-learn, XGBoost; fine-tuning (LoRA / QLoRA / PEFT) NLP & Retrieval RAG, Graph RAG, hybrid search, vector DBs (Pinecone, Weaviate, Chroma), embedding models, NER, text classification LLMOps & Eval LangSmith, Langfuse, RAGAS, LLM-as-judge, model versioning, A/B testing, prompt management Inference vLLM, TensorRT-LLM, DeepSpeed, ONNX Runtime, quantization techniques Cloud & Infra AWS (Bedrock, SageMaker, Lambda, S3) or Azure; Docker, Kubernetes, Terraform Programming Python (primary), FastAPI, SQL, Git, Bash; familiarity with TypeScript / JavaScript a plus Data & Tools PostgreSQL, Neo4j, MongoDB, Redis, Apache Kafka, Databricks, Jupyter, MLflow