Top AI & Machine Learning Jobs

Designs and executes performance studies for generative AI and machine-learning workloads across GPUs, servers, storage, networking, and software. Builds automated benchmarks, analyzes latency, throughput, scalability, utilization, and cost, identifies system bottlenecks, establishes performance baselines and release gates, and produces sizing recommendations. Collaborates with engineering, product, field, solution-sizing, and technology-partner teams to evaluate AI platforms, hardware configurations, virtualization, and distributed inference technologies.

Owns end-to-end delivery of AI features, including LLM integrations, RAG pipelines, agents, model evaluation, data and retrieval pipelines, MLOps, microservices, observability, security, and performance optimization. Builds production-ready AI systems using Python, containers, Kubernetes, vector databases, and CI/CD. Collaborates with cross-functional teams, reviews designs and code, mentors engineers, and ensures reliability, compliance, and scalability.

Leads production AI and data engineering across agentic AI, LLMs, RAG, machine learning, MLOps, data architecture, governance, and automation. Designs scalable AI solutions, defines engineering standards, oversees Databricks, Fabric, and Collibra platforms, mentors engineers, and partners with cross-functional teams. Responsibilities include LLM evaluation and fine-tuning, ML lifecycle management, data quality, responsible AI, reporting integration, technical architecture, and innovation.

Develops research methods for context discovery, retrieval, multimodal representation, reasoning, tool calling, metadata management, and trustworthy AI data pipelines. Builds and evaluates foundation models across language, vision, time series, and scientific applications. The role combines applied research with hands-on engineering, including MLOps, agentic AI, knowledge-infused learning, pipeline optimization, and collaboration with software teams and external partners.

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.

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.