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Job Summary:
Tata Consultancy Services is seeking an Applied AI Engineer with a strong background in building production AI systems. The role involves developing AI features, integrating models, and optimizing cloud infrastructure while maintaining hands-on engineering responsibilities.
Responsibilities:
• Must-Have Requirements
• Backend/Systems Experience
• 3+ years building production backend or distributed systems (pre-AI experience required)
• Production AI Systems
• Has shipped AI/LLM features serving real users at scale — not just prototypes or demos
• Agentic Systems
• Has built AI agents, skills, tools, or MCP (Model Context Protocol) integrations
• Python
• Proficient for backend development
• Secondary Language
• Working knowledge of Go, TypeScript, or Rust
• Cloud Infrastructure
• Deep experience with AWS/GCP/Azure — cost optimization, compute decisions, not just deployment
• Container & Orchestration
• Hands-on with Docker and Kubernetes — can build, deploy, debug, and scale services themselves
• LLM Integration
• Understands token economics, context limits, rate limiting, structured outputs, API failure modes
• LLM Evaluation
• Understands how to evaluate LLM outputs and the inherent challenges (non-determinism, quality measurement, regression detection)
• Hands-On Engineer
• Not just an architect — writes code, debugs production issues, deploys their own work
• Preferred / Differentiators
• Built multi-step agentic workflows with tool use and function calling
• Experience with agent orchestration frameworks (LangGraph, CrewAI, Claude Agent SDK, Google ADK, OpenAI ADK)
• Built guardrails, fallbacks, or graceful degradation for AI systems
• Streaming inference and async agent orchestration
• Cost/latency optimization: caching, batching, prompt compression
• ML observability tools: Langfuse, Arize, Braintrust, W&B
• Retrieval systems (vector search, hybrid search) — as a tool, not the focus
• Screening Questions for Candidates
• "Describe a production AI agent or skill system you built. What broke and how did you fix it?"
• "Have you built MCP servers/integrations or custom tool-use systems for LLMs?"
• "How do you evaluate whether an LLM-based feature is working well? What makes this hard?"
• "Walk me through how you'd deploy and scale an AI service on Kubernetes."
• Not a Fit If
• Primarily a model trainer/fine-tuner (we're not training models)
• AI experience is mainly academic, research, or tutorial-based
• No production systems experience (only notebooks/demos)
• Looking for entry-level role with heavy mentorship
• Background is primarily data science/analytics rather than engineering
• "Architects" who don't write or deploy code themselves
Qualifications:
Required:
• 3+ years building production backend or distributed systems (pre-AI experience required)
• Has shipped AI/LLM features serving real users at scale — not just prototypes or demos
• Has built AI agents, skills, tools, or MCP (Model Context Protocol) integrations
• Proficient for backend development in Python
• Working knowledge of Go, TypeScript, or Rust
• Deep experience with AWS/GCP/Azure — cost optimization, compute decisions, not just deployment
• Hands-on with Docker and Kubernetes — can build, deploy, debug, and scale services themselves
• Understands token economics, context limits, rate limiting, structured outputs, API failure modes
• Understands how to evaluate LLM outputs and the inherent challenges (non-determinism, quality measurement, regression detection)
• Not just an architect — writes code, debugs production issues, deploys their own work
Preferred:
• Built multi-step agentic workflows with tool use and function calling
• Experience with agent orchestration frameworks (LangGraph, CrewAI, Claude Agent SDK, Google ADK, OpenAI ADK)
• Built guardrails, fallbacks, or graceful degradation for AI systems
• Streaming inference and async agent orchestration
• Cost/latency optimization: caching, batching, prompt compression
• ML observability tools: Langfuse, Arize, Braintrust, W&B
• Retrieval systems (vector search, hybrid search) — as a tool, not the focus
Company:
Tata Consultancy Services is a business solutions company that specializes on information technology services and consulting. It is a sub-organization of Tata Group. Founded in 1968, the company is headquartered in Mumbai, IND, with a team of 10001+ employees. The company is currently Late Stage.