Job Description

Job Summary

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

Other Requirements

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