Senior AI Engineer

Responsibilities

• 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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.