Description

Senior AI/ML Engineer responsible for building and deploying production-grade Generative AI systems and classical ML pipelines.

Responsibilities

• Design and deploy Generative AI applications using LLMs (GPT-4, Claude, Gemini, open-source) and orchestrate multi-agent systems with LangGraph, LangChain, CrewAI, or AutoGen.
• Implement Retrieval-Augmented Generation (RAG), Graph RAG, and hybrid retrieval pipelines using vector databases such as Pinecone, Weaviate, Chroma, or pgvector.
• Build end-to-end ML pipelines for data ingestion, feature engineering, training, and evaluation, applying LLMOps practices like model versioning, A/B testing, and evaluation frameworks (LLM-as-judge, RAGAS).
• Optimize LLM inference using vLLM, TensorRT-LLM, or DeepSpeed and implement guardrails, content filtering, and safety mechanisms for responsible AI deployment.
• Architect scalable AI solutions on AWS (Bedrock, SageMaker) or Azure (OpenAI Service, ML Studio), containerizing applications with Docker and deploying via Kubernetes or ECS.

Required Skills

• 10+ years total experience in ML/AI or data science, with at least 2 years building and deploying GenAI/LLM applications in production.
• Strong proficiency in Python and at least one ML framework (PyTorch or 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 (Docker, Kubernetes, ECS).
• Solid understanding of transformer architectures, attention mechanisms, and modern NLP techniques.
• Experience implementing CI/CD pipelines for ML models and AI applications.
• Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, Mathematics, or a related field.

Preferred Skills

• Experience in a pharmaceutical, healthcare, or regulated industry environment with familiarity in compliance frameworks (GxP, 21 CFR Part 11, SOX).
• Background in Graph RAG, knowledge graphs, or ontology-based information extraction.
• Experience with LLM inference optimization (quantization, batching, speculative decoding) or exposure to multi-agent system design.

Key Skills

Education

Bachelor's or Master's degrees