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, India

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