[Skip to main content](#main-content) [OpenNash Jobs](https://jobs.opennash.com/) [Hiring report](/reports/hiring/) · [All Tata Consultancy Services jobs](https://jobs.opennash.com/companies/tata-consultancy-services/) · [Search jobs](/search/) Tata Consultancy Services · Cupertino, CA # Applied AI Engineer Cupertino, CA $70,000-$130,000 Operations Last seen on Tata Consultancy Services’s official careers site on August 1, 2026; it may have closed since. [Apply on Tata Consultancy Services’s careers site →](https://ibegin.tcsapps.com/candidate/jobs/418210J) [OpenNash Jobs](https://jobs.opennash.com/) / [Tata Consultancy Services](https://jobs.opennash.com/companies/tata-consultancy-services/) / Applied AI Engineer ## Job description - Must-Have Requirements Requirement Details 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 1. "Describe a production AI agent or skill system you built. What broke and how did you fix it?" 2. "Have you built MCP servers/integrations or custom tool-use systems for LLMs?" 3. "How do you evaluate whether an LLM-based feature is working well? What makes this hard?" 4. "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 Location: Cupertino, CA Salary Range: $70,000-$130,000 a year