TQUSI0689_5583 - Senior AI/ML Engineer

Senior AI/ML Engineer

Location Bangalore, India (Hybrid)

Experience 10+ years (min. 2 years in GenAI / LLM systems)

Reports To AI Engineering Lead / Principal Architect

Role Overview

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.

Basic Ask

• Tier 1 college

• Accountable

• Strong planning skills and should be able to work on the plan with the technical lead / architect

• Strong GenAI development skills alongside classical ML/NLP foundations

• Planning capability – able to lead technical planning for AI sprints and agree to delivery plans crafted by engineering leads

• Accountable, self-driven, with a bias toward shipping production-grade solutions

• Minimum 2 years hands-on experience building and deploying GenAI / LLM applications in production

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

Technical Skills Matrix

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

Qualifications Required

• Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, Mathematics, or a related field from a reputed institution

• 10+ years of total experience in ML/AI or data science roles, with a minimum of 2 years building and deploying GenAI / LLM applications in production

• Strong proficiency in Python and at least one ML framework (PyTorch / TensorFlow)

• Hands-on experience with RAG pipelines, vector databases, and LLM orchestration frameworks

• Experience deploying AI solutions on cloud platforms (AWS or Azure) with containerization

• Solid understanding of transformer architectures, attention mechanisms, and modern NLP techniques

Preferred

• Experience in a pharmaceutical, healthcare, or regulated industry environment

• Background in Graph RAG, knowledge graphs, or ontology-based information extraction

• Exposure to multi-agent system design, tool use patterns, and function calling

• Published research or contributions to open-source AI/ML projects

• Familiarity with compliance frameworks relevant to pharma (GxP, 21 CFR Part 11, SOX)

• Experience with LLM inference optimization (quantization, batching, speculative decoding)