AI Engineer – DTC Platform Engineering

Overview

Join PUMA's DTC Platform Engineering organisation as an AI Engineer in the AI-Native Engineering Enablement pillar, building production-grade LLM-powered applications and agentic workflows for product engineering teams.

What you'll do

• Construct multi-step AI agents and orchestration workflows using frameworks like LangChain, LlamaIndex, or LangGraph.
• Develop production features using foundation models via cloud AI infrastructure.
• Deploy and configure enterprise AI and agentic gateways to handle model routing, semantic caching, load balancing, rate limiting, and cost/token tracking.
• Design and secure the universal integration layer that connects AI models to enterprise databases, internal codebases, and APIs using the Model Context Protocol.
• Build semantic search, document ingestion, and Retrieval-Augmented Generation (RAG) pipelines using vector databases, embedding models, and graph databases.
• Implement vectorless approaches wherever required.
• Design and maintain advanced cognitive memory systems, including episodic memory, semantic memory, and conversational state management across sessions using graph and in-memory databases.
• Deploy, version, and monitor AI services using containerization and cloud infrastructure.
• Configure the right serving architecture based on business needs, such as real-time APIs (REST/gRPC), batch prediction jobs, or edge deployment.
• Administer central hubs (like MLflow or Weights & Biases) to track model versions, lineage, code, and artifacts.
• Manage cloud infrastructure costs by auto-scaling compute resources and selecting the right hardware (CPUs vs. GPUs) for production workloads.
• Secure model endpoints, manage access controls (IAM), and ensure data privacy compliance.
• Build Automated Evaluation (Auto-Eval) platforms and CI/CD testing harnesses to stress-test prompts, RAG pipeline outputs, and multi-agent systems at scale.
• Partner with product managers, engineering managers, SREs, Infosec team, and frontend/backend software engineers to deliver end-to-end user experiences.

What you'll need

• Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field.
• 4+ years of software development experience.
• 2+ years building, testing, and shipping agentic workflows, memory systems, and LLM features to production.
• Proven track record of deploying stateful AI systems, complex tool-calling agents, or enterprise API gateway integrations.
• Hands-on experience with multi-agent design patterns, state machines (LangGraph), and stateful memory management tools.
• Practical knowledge of configuring AI gateways for routing, fallbacks, and cost tracking.
• Experience building evaluation harnesses using frameworks like DeepEval, Ragas, etc.
• High proficiency in Python (and/or TypeScript for node-based agent runtimes).
• Experience with vector stores (Pinecone, pgvector, Milvus, Qdrant) and embedding models.
• Experience with RESTful/GraphQL APIs, Docker, Kubernetes, and enterprise cloud infrastructure (AWS, GCP, or Azure).

Nice to have

• Rust/Java understanding.

Details

• Location: Local Office Pune (Global Function).