Top AI & Machine Learning Jobs

Leads architecture strategy and governance for sovereign AI solutions across sales, presales, and delivery. Designs secure AI/ML reference architectures covering data pipelines, model lifecycle management, inference infrastructure, and governance. Advises executives and customers, manages regulatory and operational risks, drives security-by-design, leads technical reviews, develops architectural documentation, and mentors architects. Requires hands-on Kubernetes, Linux, Ansible, HPC, SLURM, NVIDIA AI Enterprise, and related infrastructure experience.

Leads a multidisciplinary engineering organization developing secure, scalable enterprise products using Generative AI and LLMs. Responsibilities include hiring and developing engineers, managing roadmaps and delivery, guiding architecture and engineering standards, overseeing quality, security, responsible AI, reliability, and stakeholder alignment. The role spans AI platforms, model customization, React experiences, AWS cloud, networking, and quality engineering, while transitioning experiments into production-ready products.

Lead customer-facing architecture for AI data platforms and AI Factory environments. Design data-centric solutions for RAG, inference, and training workflows; size GPU and storage environments; optimize data movement, retrieval, latency, throughput, cost, and reliability. Partner with customers, sales, and Product Management to qualify opportunities, influence product roadmaps, create reference architectures, and develop scalable go-to-market solutions.

Design and implement measurement frameworks, statistical analyses, and ML solutions to extract insights from customer support and conversational data. Build scalable metrics, dashboards (Power BI), and collaborate with Data Engineering to enable analytics and AI-driven automation. Mentor junior data scientists and communicate findings to business stakeholders to improve customer journeys and operational outcomes.

Design, build, and operate agentic AI workflows and MCP servers for an LLM-based network operations assistant. Implement LangGraph orchestration, RAG/memory systems, evaluation frameworks, and production integrations (Kubernetes, CI/CD, containers, FastAPI). Collaborate with domain experts, ensure observability, and drive reliability of non-deterministic AI systems at scale.

Lead end-to-end product strategy and roadmap for AI-enabled platform capabilities. Define value proposition, target segments, and business case; translate market needs into product requirements; align stakeholders and drive cross-functional delivery, adoption, and go-to-market execution.

Develop and deploy generative AI solutions that improve software development productivity, quality, and efficiency. Build LLM, RAG, agent, and automation applications integrated with developer tools, APIs, repositories, CI/CD pipelines, and engineering workflows. Evaluate model accuracy, reliability, latency, and cost; implement AI quality monitoring; and create assistants for code understanding, testing, debugging, code review, and build-failure diagnosis. Collaborate with software, DevOps, QA, and architecture teams to deliver scalable, secure AI services.

Develop and deploy AI-powered software development tools using GenAI, LLMs, RAG, agents, embeddings, and intelligent automation. Integrate AI with code repositories, CI/CD pipelines, APIs, issue trackers, and developer environments. Build assistants for code understanding, testing, debugging, code review, and failure diagnosis. Evaluate model quality, reliability, cost, and latency while developing scalable, secure services and measuring productivity impact.

Develop and execute AI-enhanced design and verification methodologies for complex digital designs from RTL through tapeout. Build AI agents, skills, APIs, and verification infrastructure; create UVM testbenches and verification plans; automate RTL generation; analyze failures; evaluate industry tools; and collaborate with design and systems engineering teams to ensure silicon quality.

Own product areas within HPC/AI system management and orchestration software: define requirements, prioritize features, create PRDs and user stories, collaborate with architects and engineering, engage customers, support releases and go-to-market, and monitor product performance to drive improvements.