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.

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 databases, internal APIs 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 Define retrieval

Requirements

— able to identify 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 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 in

Principal AI Systems Engineer