IT Risks & Control Manager

🏢 Summary: Lead and grow a Forward Deployed Engineering team delivering production-quality AI integrations, reference architectures, and proofs of concept for an AI cloud platform. The role combines people leadership, technical architecture oversight, partner engagement, and hands-on prototyping across agentic AI, inference, infrastructure, and data systems. 🗂️ Requirements: 8+ years of hands-on AI application, ML systems, or AI infrastructure engineering experience, 2+ years of engineering team leadership or management experience, Deep practical knowledge of LLM APIs, inference runtimes, orchestration frameworks, vector databases, RAG architectures, and agentic pipelines, Hands-on experience with agentic frameworks, Strong Python programming and end-to-end AI prototyping skills, Experience defining reference architectures and technical patterns, Experience building API and developer-platform integrations, Ability to work with external partner engineering teams and internal product and engineering teams, Strong technical communication skills, Authorization to work in the country of application 📃 Skills: Python, LangChain, LangGraph, CrewAI, AutoGen, LLM, RAG, FastAPI, Flask, Docker, Kubernetes, Git, AWS, GCP, Azure, vLLM, SGLang, TensorRT-LLM, Transformers, Qdrant, Weaviate, Milvus, pgvector, CUDA, TensorRT, NeMo 🏢 Description: The Role Nebius builds infrastructure for demanding AI workloads, including GPU clusters, inference runtimes, agent development environments, and data pipelines. As Manager of Forward Deployed Engineering, you will lead Forward Deployed Engineers working at the intersection of solution architecture and hands-on engineering. You will set the technical bar, develop your team’s craft, and ensure integrations, reference architectures, and proofs of concept are production-quality and strategically sound. You will remain technically engaged by reviewing architectures, unblocking complex scoping problems, and occasionally prototyping. Remote work is available within the United States. Responsibilities People & Team Leadership • Hire, develop, and retain Forward Deployed Engineers across agentic, inference, infrastructure, and data focus areas. • Set expectations for technical quality and partner engagement; coach engineers toward those standards. • Run structured 1:1s, provide direct, actionable feedback, and own each team member’s growth. • Build a culture where moving quickly and building well are aligned. • Partner with Recruiting to define requirements for FDE roles and source candidates from the AI builder community. Technical Oversight & Architecture • Review and elevate integration architectures, proofs of concept, reference patterns, and partner scoping assessments. • Act as a technical escalation point for complex partner engagements and contribute hands-on when needed. • Maintain a high bar for the reference architecture library, emphasizing patterns that should be emulated. • Stay current with the AI tooling ecosystem to guide the team’s technical judgment. Ecosystem Presence • Represent the organization at hackathons, open-source communities, and technical events. • Build public demos, reference architectures, and integrations. • Track new developments in the AI tooling ecosystem and assess their impact on the platform stack. Platform Focus Areas Depending on background and mutual fit, focus on one or more areas: • Agentic: agent frameworks, memory systems, tool integration, orchestration, MCP, and guardrails. • Managed Inference: inference runtimes, model serving, optimization tooling, speculative decoding, and KV-cache routing. • IaaS / Managed Infrastructure: cloud-native integrations, GPU orchestration, and enterprise platform connectors. • Data: vector databases, retrieval systems, RAG architectures, data-pipeline integrations, and synthetic-data tooling. Partner & Internal Stakeholder Engagement • Engage with senior partner engineering leaders and founding CTOs on strategic matters. • Translate field insights into actionable product requirements for platform teams. • Represent the FDE function in platform planning discussions as the technical voice of ecosystem integration. • Work with ISV, SI, and field teams to scale solution adoption and drive revenue after integrations are ready. Required Qualifications • 8+ years of hands-on engineering experience in AI application development, ML systems, or AI infrastructure. • 2+ years managing or leading engineers, with a record of developing technical talent. • Deep working knowledge of LLM APIs, inference runtimes, orchestration frameworks, vector databases, RAG architectures, and agentic pipelines gained through shipping products. • Hands-on experience with agentic frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or equivalent. • Strong Python skills and the ability to prototype end-to-end AI systems quickly. • Experience defining reference architectures and technical patterns. • Ability to rapidly move from idea to working prototype and deliver meaningful work under time pressure. • Experience building integrations across APIs and developer platforms. • Ability to work simultaneously with external partner engineering teams and internal Product and Engineering teams. • Strong technical communication skills for explaining architecture decisions and integration findings to technical and non-technical stakeholders. Preferred Qualifications • Experience with vLLM, SGLang, TensorRT-LLM, speculative decoding, quantization, batching, and KV-cache routing. • Familiarity with CUDA, TensorRT, NeMo, or equivalent NVIDIA software. • Experience with multimodal AI models for vision-language, speech, or structured data. • Recent placement or win at a major AI hackathon. • Experience as a developer advocate, solutions engineer, or technical partner manager at an AI platform or developer-tooling company. • Early engineering experience at a YC-backed AI startup. • Open-source projects or public demos with meaningful community adoption. • Proficiency with Docker, Kubernetes, and Git. Preferred Technical Stack • Languages: Python. • ML frameworks: vLLM, SGLang, TensorRT-LLM, Transformers, and OpenAI / Anthropic SDKs. • Agentic frameworks: LangChain, LangGraph, CrewAI, AutoGen, smolagents, or equivalent. • Vector databases: Qdrant, Weaviate, Milvus, and pgvector. • API and web frameworks: FastAPI and Flask. • DevOps: Kubernetes, Docker, and Git. • Cloud platforms: AWS, GCP, and Azure. Key Employee Benefits • 100% company-paid medical, dental, and vision coverage for employees and families. • 401(k) plan with up to 4% company match and immediate vesting. • 20 weeks of paid parental leave for primary caregivers and 12 weeks for secondary caregivers. • Remote-work reimbursement of up to $85 per month for mobile and internet. • Company-paid short-term disability, long-term disability, and life insurance. Compensation Actual compensation is based on job-related factors, including experience, skills, qualifications, hiring level, and geographic location, consistent with applicable law. Starting base compensation range: $225,800–$281,000 USD. Benefits & Perks • Competitive compensation. • Career growth and learning opportunities. • Flexibility and ownership. • Collaborative and innovative culture. • Opportunity to work on impactful AI projects. • International environment and talented teams. Work Environment Fast moving; bold thinking; constant growth; meaningful impact; trust and real ownership; opportunity to shape the future of AI. Equal Opportunity and Work Authorization Equal employment opportunities are provided without discrimination based on protected characteristics under applicable law. Applicants must be authorized to work in the country where they apply and provide proof of employment eligibility as a condition of hire. Accommodations during the application process are available upon request.