Top AI & Machine Learning Jobs

Build and deploy production-grade AI agents, workflow automations, and internal applications that improve enterprise processes. Partner with business, operations, engineering, and security teams to identify use cases, design reliable agent architectures, prototype with LLMs and agent frameworks, measure impact, and drive adoption. The role requires strong systems engineering, API, data, and workflow design skills, along with the ability to translate ambiguous business problems into scalable technical solutions.

Leads creative technology initiatives by building AI agent pipelines, MCP servers, plugins, generative video systems, and voice agents. Designs AI architectures, prototypes emerging technologies, develops scalable creative systems, and optimizes tools and campaigns using tracing and agent analytics. Partners with clients and cross-functional teams, presents to executive stakeholders, mentors team members, and balances brand, audience, business, creative, and technical objectives.

Build and productionize computer vision, 3D perception, and LLM-powered systems using LiDAR and imagery to assess infrastructure. Own AI models throughout data preparation, training, evaluation, deployment, and monitoring. Develop agentic workflows, MCP integrations, and customer-facing AI tools while defining performance metrics and evaluating emerging technologies.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Founding engineer responsible for building and scaling 0-to-1 AI-powered products for wealth management. Owns end-to-end user experiences, partners with clients and internal teams, establishes architecture and product strategy, and develops applied AI systems including agents, harnesses, models, workflows, memory, context, and evaluations.

Conduct research on how consumer LLMs search, reason, and synthesize information; design experiments and causal inference analyses; support product and engineering decisions with rigorous measurement; and build data pipelines and recurring evaluations that turn evolving research findings into product capabilities.

Develop production software using AI-first engineering practices. Translate requirements into prompts, implementation plans, and acceptance criteria; generate and validate AI-assisted code; identify hallucinations, security issues, and logic gaps; create automated tests; and ensure architecture, performance, documentation, and production readiness. Build reusable prompt workflows, collaborate with product and technical teams, document decisions, and guide engineers in adopting AI-native development practices.

Designs and leads end-to-end AI/ML architectures for clients across industries. Advises stakeholders on AI strategy, leads discovery workshops, oversees production delivery, and establishes model lifecycle and AgenticOps practices. The role also supports presales, develops reusable AI offerings, and defines responsible AI governance, architecture standards, and best practices. Collaboration with data scientists, engineers, and developers is central to delivering scalable, secure, cloud-based solutions.

Develop data science systems for motion imagery and full-motion video programs. Analyze aircraft operations and perform multi-object tracking on live video. Work with operational, archived, and live data in Elastic, Oracle, and Kafka; develop Python-based solutions and deploy code to Kubernetes using current GPU technologies. Subject matter expertise is welcomed, while the role supports mission-focused government capabilities.

Build and deploy spatial AI capabilities for generative home floor plans. Responsibilities include training and adapting ML models, creating APIs and evaluation pipelines, improving reproducibility, managing latency and cost, extending research into product features, partnering on MLOps and deployment, and mentoring engineers and researchers.