AI Systems Engineer

About the role

We are hiring multiple AI Systems Engineers to join Mobius Research Lab in India and help build a new class of AI platform: one that goes beyond chatbots, shallow agents, and one-off automation workflows.

This role is for a deeply technical engineer or applied researcher who wants to work on the foundations of emerging AI systems: structured AI labor, knowledge representation, agent orchestration, graph-based reasoning, secure runtime execution, workflow compilation, model integration, and enterprise-grade validation.

You will help build systems that can ingest complex real-world information, convert it into structured machine-understandable form, reason over it, produce executable plans, validate outcomes, and operate safely across modern cloud and AI infrastructure.

This is not an ordinary AI application role. It is a chance to work close to the platform layer where the next generation of AI systems will be defined: reliable, composable, governable, secure, and capable of operating at world scale.

Who we are looking for

We are looking for candidates who combine deep academic preparation with hands-on engineering ability. The ideal candidate has strong foundations in computer science, AI, distributed systems, data systems, graph reasoning, or secure platform engineering, and is excited by unusually complex, open-ended technical challenges.

Education requirement: PhD or M.Tech only, from premier or highly reputed institutions with strong computer science, AI, systems, mathematics, data science, or engineering programs.

Experience requirement: 5 to 7 years of relevant experience in AI systems, backend platforms, distributed systems, data systems, ML infrastructure, knowledge graphs, security, or enterprise automation.

Research depth: Ability to read dense technical material, reason from first principles, formulate abstractions, and convert research-grade ideas into working systems.

Builder mindset: Strong ability to prototype quickly, validate rigorously, harden what matters, and take responsibility for correctness.

Ambition: A desire to do extraordinarily complex and challenging work with the potential to make an impact on the world stage.

Candidates from institutions such as IISc, IITs, IIIT-H, ISI, CMI, top NITs, BITS Pilani, and internationally comparable universities are strongly encouraged to apply. Equivalent evidence of exceptional research and engineering depth may be considered only where the academic bar is clearly met.

What you will work on

You will work on the core platform layer that turns AI reasoning into durable, auditable, executable capability. Your work may include:

AI compiler and transformation pipelines: Build pipelines that take documents, APIs, workflows, schemas, policies, and domain knowledge, then transform them into structured internal representations that downstream AI agents and services can use.

Structured LLM labor systems: Design systems where LLMs perform accountable work: decomposition, classification, mapping, extraction, schema generation, validation, repair, synthesis, and explanation.

Knowledge ingestion and representation: Create pipelines that ingest enterprise documents, OpenAPI specs, BPMN workflows, JSON/YAML files, standards, contracts, and operational data, then convert them into typed knowledge graphs, semantic objects, lineage records, and reusable execution context.

Agentic orchestration: Build multi-agent and tool-using systems that can plan, call tools, coordinate tasks, manage intermediate state, recover from failure, and produce auditable outputs.

Graph reasoning and validation: Develop graph validators, compatibility checkers, state-transition checks, provenance verifiers, dependency analyzers, and repair workflows to make sure AI-generated structures are internally consistent and execution-ready.

Secure AI runtime integration: Connect AI reasoning systems to execution surfaces such as Kubernetes, workflow engines, GitOps, serverless tasks, GPU/TPU jobs, confidential VMs, policy engines, and enterprise APIs.

Evaluation and observability: Build test harnesses, evaluation suites, trace systems, model-output validators, regression checks, quality gates, and metrics that make AI behavior measurable and improvable.

What great looks like

A strong candidate will be able to:

Take a messy business document, API spec, standard, or workflow and design a structured representation for it.

Build an LLM-powered extraction or transformation pipeline with validation and repair loops.

Design a graph model that captures entities, relationships, dependencies, provenance, and execution state.

Create an evaluation harness that catches bad model outputs before they enter production.

Connect agentic workflows to real tools and APIs safely.

Build clear interfaces between reasoning systems and runtime systems.

Think deeply about how AI-generated outputs should be versioned, traced, audited, and trusted.

Work across AI, backend, infrastructure, data, and security boundaries.

Why this role matters

The first wave of AI engineering was about using models. The next wave is about building systems that make model labor reliable.

That requires a different kind of engineer: someone who understands software architecture, data modeling, distributed systems, security, AI behavior, and runtime execution.

This role sits at that intersection. You will not simply build prompts. You will help build the substrate that allows AI to become dependable infrastructure: structured, testable, traceable, governable, and composable.