Required AI Platform Engineering Team Lead – Sovereign AI Engineering
We're building AI that nations own and control, deployed where almost no one else can operate. ingesting and structuring complex data, and driving practical actions that can literally impact the lives of billions of people around the world. This role helps make that real.
The Dream Job:
It starts with you – a technical leader driven to build both the agentic AI platform and the engineering team behind it. You care about backend quality, platform reliability, and growing engineers through real ownership. We are AI-first across the board – every team builds and operates agents. You'll set the technical direction for the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation infrastructure, tool-calling systems, and retrieval pipelines. Without this platform, agents don't ship – you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments. You stay close enough to the codebase to debug production incidents, unblock your engineers, and make sound architecture calls.
If you want to make a meaningful impact, join our mission and lead the team that builds the agentic AI platform driving Sovereign AI products – this role is for you.
Responsibilities
Architect and evolve the AI platform – agent orchestration, LLM gateways, context engineering pipelines, evaluation infrastructure, tool-calling systems, and retrieval pipelines – through RFCs, prototypes, and design reviews.
Lead and grow a small team of AI Engineers building the agent framework, production backend services, and AI platform infrastructure – hire, mentor, pair on hard problems, and raise the bar through hands-on code and design reviews.
Contribute to critical systems, debug production incidents, and maintain enough codebase context to make sound technical calls.
Own reliability across AI and agent services – set and enforce SLAs, build observability for non-deterministic systems, and harden tool execution environments for cost and security.
Set the standard for AI engineering practices – agent testing strategies, evaluation frameworks with human-in-the-loop oversight, retrieval quality benchmarks, and CI/CD for AI systems.
Work closely with ML Platform, Data Platform, DevOps, Data Science, and Product teams across the Applied AI Engineering group – ensure the AI platform evolves to serve teams building agentic workflows across the organization.
Measure and improve developer experience – deploy friction, onboarding time, CI turnaround – as seriously as system performance.
We're building AI that nations own and control, deployed where almost no one else can operate. ingesting and structuring complex data, and driving practical actions that can literally impact the lives of billions of people around the world. This role helps make that real.
The Dream Job:
It starts with you – a technical leader driven to build both the agentic AI platform and the engineering team behind it. You care about backend quality, platform reliability, and growing engineers through real ownership. We are AI-first across the board – every team builds and operates agents. You'll set the technical direction for the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation infrastructure, tool-calling systems, and retrieval pipelines. Without this platform, agents don't ship – you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments. You stay close enough to the codebase to debug production incidents, unblock your engineers, and make sound architecture calls.
If you want to make a meaningful impact, join our mission and lead the team that builds the agentic AI platform driving Sovereign AI products – this role is for you.
Responsibilities
Architect and evolve the AI platform – agent orchestration, LLM gateways, context engineering pipelines, evaluation infrastructure, tool-calling systems, and retrieval pipelines – through RFCs, prototypes, and design reviews.
Lead and grow a small team of AI Engineers building the agent framework, production backend services, and AI platform infrastructure – hire, mentor, pair on hard problems, and raise the bar through hands-on code and design reviews.
Contribute to critical systems, debug production incidents, and maintain enough codebase context to make sound technical calls.
Own reliability across AI and agent services – set and enforce SLAs, build observability for non-deterministic systems, and harden tool execution environments for cost and security.
Set the standard for AI engineering practices – agent testing strategies, evaluation frameworks with human-in-the-loop oversight, retrieval quality benchmarks, and CI/CD for AI systems.
Work closely with ML Platform, Data Platform, DevOps, Data Science, and Product teams across the Applied AI Engineering group – ensure the AI platform evolves to serve teams building agentic workflows across the organization.
Measure and improve developer experience – deploy friction, onboarding time, CI turnaround – as seriously as system performance.
Requirements:
6+ years in backend software engineering, with 4+ years focused on production systems that integrate AI/ML models or LLMs.
2+ years leading an engineering team – hiring, mentoring, conducting design reviews, and shipping alongside your team.
Engineering craft – Strong Python, Go, or Java, system architecture, API design, testing, and secure coding practices.
Agentic systems & LLM integration – Deep understanding of agent orchestration, tool-use architectures, LLM integration patterns, context engineering, and frameworks like LangGraph or similar, or custom-built equivalents
Backend & platform engineering – Experience building and operating production APIs, services, and platform infrastructure at scale; comfortable working with relational databases, message queues, and event-driven architectures
RAG & retrieval – Experience with production RAG pipelines, vector databases, embedding systems, and retrieval quality
Evaluation & observability – Experience building LLM and agent eval infrastructure, monitoring AI quality, and observability for non-deterministic systems
Nice to Have:
Platform & infra – Kubernetes, AWS, Terraform or similar IaC, CI/CD, service architecture, incident management
Experience with MCP or similar tool-use protocols for agent-to-service communication.
6+ years in backend software engineering, with 4+ years focused on production systems that integrate AI/ML models or LLMs.
2+ years leading an engineering team – hiring, mentoring, conducting design reviews, and shipping alongside your team.
Engineering craft – Strong Python, Go, or Java, system architecture, API design, testing, and secure coding practices.
Agentic systems & LLM integration – Deep understanding of agent orchestration, tool-use architectures, LLM integration patterns, context engineering, and frameworks like LangGraph or similar, or custom-built equivalents
Backend & platform engineering – Experience building and operating production APIs, services, and platform infrastructure at scale; comfortable working with relational databases, message queues, and event-driven architectures
RAG & retrieval – Experience with production RAG pipelines, vector databases, embedding systems, and retrieval quality
Evaluation & observability – Experience building LLM and agent eval infrastructure, monitoring AI quality, and observability for non-deterministic systems
Nice to Have:
Platform & infra – Kubernetes, AWS, Terraform or similar IaC, CI/CD, service architecture, incident management
Experience with MCP or similar tool-use protocols for agent-to-service communication.
This position is open to all candidates.
