Senior AI Engineer

Reports To

Director, AI & IT

What You Will Be Doing

As a Senior GenAI Engineer, you will:

• Architect and build LLM‑based applications such as copilots, chatbots, and AI agents
• Design and optimize RAG and grounded AI systems using enterprise data
• Lead development of agentic workflows with tool/function calling and multi‑step reasoning
• Own backend services and APIs for AI inference and orchestration
• Drive LLMOps / GenAIOps practices (evaluation, monitoring, CI/CD, versioning)
• Optimize AI systems for quality, cost, latency, and reliability
• Apply and advocate for Responsible AI and security‑by‑design
• Mentor engineers and influence AI engineering best practices
• Partner with product, platform, and security teams to shape AI strategy

What You Will Bring to ChargePoint

• Deep expertise in Python, FastAPI, Django, and modern backend frameworks for AI service development
• Hands-on experience with LLM engineering: LangChain, LangGraph, Amazon Bedrock/OpenAI APIs, prompt engineering, and RAG architectures
• Strong experience with Elasticsearch including vector search, hybrid search (BM25 + dense embeddings), and semantic retrieval
• Proficiency with vector databases (Qdrant, ChromaDB, Pinecone) and embedding-based retrieval systems
• Experience building production LLM systems with focus on low-latency inference, caching strategies, and observability
• Strong foundation in distributed systems design, microservices architecture, and event-driven patterns
• Ability to balance speed, quality, and risk in production AI deployments
• Passion for building scalable, maintainable, and responsible AI platforms
• Strong communication skills with engineers, product managers, and leadership

Requirements

• 6+ years of professional software engineering experience
• Strong development skills in Python; experience with Java, FastAPI, Django, and modern backend frameworks for AI service development
• Extensive hands-on experience with LLMs and generative AI systems
• Strong experience with RAG, embeddings, Elasticsearch including vector and hybrid search, and prompt engineering
• Experience building Copilot-style, conversational, and agent-based AI systems
• Strong understanding of distributed systems, APIs, microservices architecture, and event-driven patterns
• Experience with cloud platforms (AWS/GCP), containerization (Docker, Kubernetes), and CI/CD pipelines
• Familiarity with LLMOps and MLOps practices

Good to Have

• Experience with AI governance, compliance, or regulated enterprise environments
• Experience with vector databases and retrieval optimization
• Prior ownership of AI systems running in production at scale

Location

Remote, India