Principal Architect/Vice President

The mission

We are looking for a Vice President of AI Systems Engineering to lead the engineering foundation of Mobius Research Lab in Hyderabad. This is the topmost engineering leadership role in India for a new class of AI platform: one that moves beyond packaged software toward an intelligently connected world, where AI systems can understand, coordinate, execute, and improve complex work across enterprises, infrastructure, knowledge, and economic activity.

This leader must be able to operate at the level of the best frontier AI platforms and global product companies, while also seeing beyond the current software model. The future is not only applications sold as packages. It is connected intelligence: systems that can reason over knowledge, coordinate model labor, operate securely at planetary scale, and create measurable economic value.

Why this role exists

Mobius needs a leader who can turn a deeply ambitious AI engineering roadmap into working systems, high-performance teams, and production-grade capability. The VP of AI Systems Engineering will translate advanced research and architecture into real platforms, services, developer workflows, validation systems, and runtime infrastructure.

This role is for someone who has already built or led serious engineering in world-class environments, and now wants to build something more foundational: the engineering layer for AI-native work, AI-native platforms, and the emerging intelligence economy.

What you will own

Own the end-to-end AI systems engineering roadmap for Mobius Research Lab India, from research prototypes to hardened platform capability.

Lead architecture and engineering execution for distributed AI systems, structured model labor, knowledge representation, runtime orchestration, secure compute, and platform integration.

Build and mentor a high-performance team of young AI systems engineers, research engineers, platform engineers, and infrastructure engineers.

Establish engineering standards for correctness, security, scalability, observability, validation, reproducibility, and production readiness.

Design systems that can ingest complex knowledge, reason over it, transform it into structured representations, produce executable plans, and validate outcomes.

Create the platform discipline required to safely connect AI systems to APIs, workflows, cloud infrastructure, data systems, and enterprise operating environments.

Drive the use of modern accelerator and secure-compute infrastructure, including GPU/TPU workloads, confidential compute, distributed orchestration, and cost-aware execution.

Partner with product, research, business, and founder-level leadership to convert strategic vision into engineering milestones and measurable delivery.

The kind of systems you will help build

The team will work on foundational AI platform systems, not shallow AI features. The work will involve:

AI transformation pipelines that convert documents, APIs, workflows, policies, schemas, operational data, and domain knowledge into structured internal representations.

LLM labor systems where models perform accountable work such as decomposition, extraction, classification, synthesis, schema generation, validation, repair, and explanation.

Knowledge graph and semantic object systems that preserve entities, relationships, lineage, provenance, and execution context.

Agentic orchestration layers with typed state, tool access, planning, retries, failure recovery, memory, and auditability.

Secure runtime bridges from AI reasoning to Kubernetes, cloud services, workflow engines, serverless tasks, GPU/TPU jobs, and enterprise APIs.

Evaluation, trace, and observability systems that make AI-generated outputs measurable, inspectable, replayable, and trustworthy.

Policy, security, and governance controls that allow AI systems to act safely in high-value enterprise environments.

Technical depth required

The ideal candidate has hands-on depth and leadership credibility across several of the following areas:

Large-scale distributed systems and platform architecture

AI/LLM systems, agentic workflows, RAG, model routing, evaluation

Knowledge graphs, semantic search, document intelligence, metadata and lineage

Secure systems, identity, policy-as-code, secrets, KMS, attestation, confidential compute

API platforms, OpenAPI/AsyncAPI, schemas, contract testing, event-driven systems

Observability, tracing, quality gates, reliability engineering, cost-aware operations

Kubernetes, GitOps, workflow orchestration, cloud-native runtime systems

GPU/TPU workloads, ML infrastructure, model serving, batch and real-time inference

Python, TypeScript, Go, Rust, Java, data systems, graph stores, vector stores

Engineering leadership in high-velocity product, cloud, infra, or AI platform organizations

Experience profile

18+ years of engineering experience preferred, with significant time spent building large-scale products, platforms, infrastructure, cloud systems, AI systems, or developer platforms.

At least 5+ years leading senior engineering teams, ideally across AI, platform, distributed systems, infrastructure, data, or cloud-native product engineering.

Experience in a top-tier product company, frontier AI platform, hyperscaler, global SaaS platform, internet-scale company, deep-tech company, or major infrastructure platform is strongly valued.

Demonstrated ability to build systems used by large numbers of users, developers, enterprise customers, workloads, or machines.

Strong judgment on architecture, security, scalability, technical debt, team design, and delivery sequencing.

Ability to represent engineering at founder, board, partner, customer, and strategic ecosystem levels when required.

Education and research orientation

A PhD, M.Tech, or equivalent advanced technical degree from a premier institution is strongly preferred. We are especially interested in candidates from highly reputed institutions such as IITs, IISc, IIIT-H, leading NITs, BITS Pilani, top global universities, or similarly rigorous research and engineering environments.

Academic pedigree alone is not enough. The person must also have demonstrated engineering impact: systems shipped, platforms scaled, teams built, production failures survived, and technical complexity mastered.

This role is ideal for someone who combines research-level curiosity with product-company execution discipline.

Closing pitch

Mobius Research Lab India is looking for a rare engineering leader: someone who can combine the scale discipline of the world's best product platforms, the technical depth of frontier AI systems, and the imagination to build beyond today's software categories.

If you want to lead a team building the engineering foundation for the intelligence economy, this is the role.