ATARI- Principal AI System Engineer

About Us

• : 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. We're proud of the team we've assembled so far, and we’re just getting started. 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. Whether you're helping launch a new game, keeping our
• infrastructure secure, or supporting day-to-day operations, your work here matters. Join us as we continue to
• grow Atari India and build the future of a legendary brand.

Position: Principal AI Systems Engineer

Experience: 8+ Years

Location: Netaji Subhash Place, Pitampura, New Delhi.

Employment Type: Full-Time (Hybrid)

Reports to: Senior Director of Technology, Indi

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About the Role

• Architect, build, and own AI systems that automate expert-intensive technical workflows end-to end —
• from CLI frameworks, MCP servers, and agent tooling through to production deployment, business
• outcome tracking, and continuous improvement. You solve real business problems with AI, ensure
• solutions are fully implemented and adopted, and measure whether they are actually working.

Responsibilities

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