Principal AI System Engineer

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

Apply Here: https://forms.gle/nYJjdUXqPRfsWVje9

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

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