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ATARI- Principal AI System Engineer

Delhi, IN · Principal · Full-time

Sponsorship not specifiedDetected 48 days ago
PythonAWSGCPAzureCloud PlatformsLLMsRAGLangGraphAI OrchestrationSystems EngineeringManual TestingUnityUnreal EngineLeadershipCommunication

About the role

  • We're proud of the team we've assembled so far, and we're just getting started.
  • Whether you're helping launch a new game, keeping our infrastructure secure, or supporting day-to-day operations, your work here matters.
  • Position: Principal AI Systems Engineer Experience: 8+ Years Location: Netaji Subhash Place, Pitampura, New Delhi.

Responsibilities

  • orchestration, structured prompting, context assembly, schema validation, and retry strategies ● Integrate AI systems with external tooling - version control, build pipelines, SDKs, compliance ●
  • how domain knowledge, runtime state, retrieved documents, and tool outputs compose into the precise input each pipeline stage needs ●
  • curated document corpora, rule sets, decision trees, and validation reference libraries ●

Requirements

  • system prompts, few-shot design, chain-of-thought, token budget management, and prompt versioning ● Proficiency with LLM orchestration frameworks - LangChain, LangGraph, LlamaIndex, AutoGen, or equivalent ●

Skills

  • System Architecture ●
  • test suites, regression benchmarks, LLM-as-judge, and production quality monitoring ●

Benefits

  • automation rate, time-to-completion, and human intervention rate Bonus Points ●

Company info

  • Founded in 1972, Atari is one of the world's most iconic consumer brands and a pioneer in the video game industry, known for creating classics like Pong, Asteroids, and Centipede.
  • Today, Atari Inc. continues to build on its legacy by developing games, hardware, and experiences that honor the past while driving innovation for the future.
  • Over the past two years, we've been building Atari India, a growing team that plays a critical role in supporting our global operations.
  • As part of a lean, high-impact organization, the team in India works closely with colleagues in North America and Europe on projects that move the company forward.
  • Join us as we continue to grow Atari India and build the future of a legendary brand.
  • You solve real business problems with AI, ensure solutions are fully implemented and adopted, and measure whether they are actually working.
  • System Architecture ● Own end-to-end architecture of AI automation systems: workflow decomposition, component communication, human checkpoints, and failure behaviour ● Design and build internal CLI frameworks, reusable libraries, and agent scaffolding ● Author and maintain agent instruction files (SKILL.md, CLAUDE.md, system prompts) and MCP server definitions ● Configure Claude Code and Codex CLI environments: MCP wiring, tool permissions, slash commands, and engineering standards ● Evaluate and document architectural trade-offs across reliability, latency, cost, and maintainability Pipeline Development ● Build production-grade AI pipelines in Python: orchestration, structured prompting, context assembly, schema validation, and retry strategies ● Integrate AI systems with external tooling - version control, build pipelines, SDKs, compliance ● Design context assembly: how domain knowledge, runtime state, retrieved documents, and tool outputs compose into the precise input each pipeline stage needs ● Build and operate multi-agent systems: orchestrator-worker patterns, agent memory, structured handoffs, and conflict resolution Prompt & Context Engineering ● Design, version, and maintain system prompts and agent instructions as first-class engineering artefacts ● Own output schema design and prompt regression testing with a maintained ground-truth eval set ● Engineer context windows with precision - balancing accuracy, token cost, and latency through compression and selective retrieval ● Partner with the RAG Engineer to define retrieval requirements - what knowledge is needed, under what conditions, and at what granularity ● Build and maintain structured runtime knowledge assets: curated document corpora, rule sets, decision trees, and validation reference libraries ● Work with domain experts to translate specialist knowledge into agent behaviour: decision logic, edge cases, and failure modes Evaluation & Reliability ● Build and own the evaluation framework: test suites, regression benchmarks, LLM-as-judge pipelines, and per-stage quality metrics ● Implement production monitoring using LangFuse, Arize, or equivalent - latency, token usage, success rates, and output quality drift ● Run structured failure analysis and implement targeted fixes across context assembly, orchestration, and tool integration ● Define automation rate as a first-class metric and report on business effectiveness of deployed systems Governance & Technical Leadership ● Implement full audit trails - inputs, tools called, outputs, and human review triggers ● Enforce versioning of all agent instructions and system prompts as engineered artefacts with controlled rollout ● Set the technical standard for AI development across the organization - architecture patterns, eval practices, and quality gates ● Collaborate with engineering, product, and domain teams; engage leadership on roadmap priorities and technical risk.
  • Requirements ● Proven track record of building production AI automation systems from scratch - end-to-end from architecture through deployment. ● Hands-on expertise with Claude Code, Codex CLI, Cursor, or equivalent - including MCP server configuration and agent instruction authoring ● Experience designing and deploying MCP servers and custom tools: tool schema, authentication, and permission boundaries ● Experience building internal CLI frameworks, agent scaffolding, and reusable libraries that others build on. ● Experience creating internal tooling and automation that measurably improved engineering team efficiency - reducing manual processes and accelerating workflows ● Experience working with data scientists and domain experts to implement AI solutions that measurably improved team productivity ● Deep prompt and context engineering: system prompts, few-shot design, chain-of-thought, token budget management, and prompt versioning ● Proficiency with LLM orchestration frameworks - LangChain, LangGraph, LlamaIndex, AutoGen, or equivalent ● Experience building AI evaluation frameworks: test suites, regression benchmarks, LLM-as-judge, and production quality monitoring ● Production Python engineering: modular, testable, well-logged code with proper error handling ● Cloud platform experience (AWS, Azure, or GCP): deploying and monitoring AI workloads with containerisation ● Experience integrating AI systems with external APIs - tool definition, permission management, and failure handling ● Experience defining and tracking AI productivity metrics: automation rate, time-to-completion, and human intervention rate Bonus Points ● Experience in gaming: game development pipelines, Unity/Unreal engine architectures, or platform certification processes ● Familiarity with game engine scripting, asset pipelines, or platform SDKs (Xbox GDK, PlayStation SDK, or similar) Shift Timings: 9AM TO 6PM IST
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