Top AI & Machine Learning Jobs

Build a personalized provider-recommendation pipeline using LLMs, semantic search, recommendation algorithms, agent workflows, and multi-source knowledge integration. Responsibilities include extracting user requirements, retrieving and ranking providers, connecting backstage decision pipelines, developing evaluation datasets, analyzing failures and feedback, and improving quality, reliability, and latency. The intern will collaborate with engineering and product teams from design through launch.

Build production AI-powered software for hiring, finance, capital planning, contract analysis, and operational workflows. Develop LLM features, MCP servers, and agentic systems; embed with finance, legal, recruiting, accounting, and operations teams; translate expert workflows into structured data and automated processes; and ship autonomous, user-friendly products rapidly.

Own backend architecture, code, deployments, monitoring, and on-call for AI shopping agents that perform live purchases. Build resilient checkout automation, scalable product ingestion infrastructure, embedding-based search and ranking, and systems that automatically detect and recover from failures. The role requires strong Python, end-to-end production ownership, and comfort with ambiguous, adversarial systems. Experience with AI agents, LLMs, machine learning, e-commerce, payments, or fraud infrastructure is preferred.

Deploys agentic AI solutions for construction customers from requirements through production, including custom architecture, data pipelines, knowledge graphs, adoption, and value realization. Leads workshops, business architecture reviews, and executive engagements; builds moderately complex AI solutions; manages customer data readiness and governance; collaborates with engineering, product, account management, and customer success teams; and travels to customer sites for deployment and adoption activities.

Own the production lifecycle for machine learning and LLM-backed systems, including training and post-training pipelines, inference serving, model releases, monitoring, retrieval infrastructure, and cost and latency optimization. Partner with AI researchers and engineering teams to move prototypes into reliable production capabilities across customer tenants. Develop ETL and GraphQL features, enforce tenant isolation, and support deployments in regulated or customer-controlled environments.

Advises clients on applying Microsoft AI tools to business needs, identifies high-value use cases, designs Copilot agents and AI-enabled solutions, facilitates readiness workshops, supports adoption and change management, and measures productivity and business value. The role also contributes to AI governance, training content, stakeholder presentations, presales solutioning, prompt engineering, and ongoing Microsoft AI strategy updates.

Develop and productionize fraud detection machine learning models, engineer features from large-scale behavioral and transaction data, and monitor model performance. Lead complex fraud investigations by reconstructing attacker behavior, identifying patterns, producing technical reports, and translating findings into detection improvements and customer-facing fraud trend insights. Collaborate with product, engineering, operations, legal, executives, and customers.

Configure, implement, test, and optimize AI-powered insurance solutions across FAST and cloud AI platforms. Responsibilities include prompt engineering, model selection, troubleshooting, performance monitoring, documentation, customer engagement, usage analysis, and translating business needs into technical configurations. Collaborate with product owners, engineers, and customers on AI implementations, agile ceremonies, backlog refinement, and product improvements.

Leads data engineering, Palantir Skywise architecture, and AI solution development for Airbus manufacturing and operational environments. Builds scalable pipelines, analytical workflows, machine learning and generative AI applications, and enterprise data foundations. Oversees Alteryx and Tableau platforms, establishes governance, mentors technical teams, translates business challenges into data products, and supports AI Center of Excellence initiatives. The role is onsite 60% across several U.S. locations and requires approximately 10% travel.

Design, build, and deploy enterprise-grade LLM and Generative AI solutions (RAG, agents, vector DBs, semantic search). Architect APIs, integrations, and production pipelines; evaluate emerging AI tools; implement governance, security, observability, and reusable AI platform components to drive business adoption.