Top AI & Machine Learning Jobs

Lead design, build, and operate scalable GenAI backend services and APIs (Python/FastAPI). Integrate LLM providers, vector stores, and Aurora PostgreSQL for RAG workflows. Implement guardrails, observability, CI/CD, testing, and on-call support. Partner with cross-functional teams and produce documentation to enable safe, multi-tenant GenAI platform adoption.

Applies advanced statistical and machine learning methods to B2B supply optimization, builds datasets and models, designs experiments, creates visualizations and dashboards, and translates analysis into business recommendations. Leads analytical workstreams, automates reporting, documents reproducible code, communicates with senior stakeholders, and mentors junior analysts.

Define and deliver AI-powered analytics and decision-support products. Set strategy and roadmaps, partner with engineering and analytics teams to build LLM/RAG-driven conversational and agentic experiences, translate customer needs into requirements, use data to measure performance, and ensure trust, governance, privacy, and responsible AI practices.

Build, scale, deploy, monitor, and operate machine learning systems in high-scale production environments. The role develops batch and streaming inference pipelines, evaluates model performance and drift, implements guardrails, troubleshoots reliability issues, optimizes compute and memory usage, and applies MLOps practices. It also mentors junior engineers, collaborates cross-functionally, and uses generative AI and AI-assisted engineering tools responsibly.

Lead applied ML scientists and managers to build production-grade models and optimization systems for marketing (bidding, capital allocation, ROAS). Own roadmap, OKRs, hiring, cross-functional partnerships, MLOps, experimentation, and production delivery at scale while driving measurable business impact.

Operate and optimize AI-enabled production systems in AWS, focusing on reliability, observability, security, automation, and scalability. Build infrastructure-as-code tooling, runbooks, deployment processes, incident response, and recovery workflows. Collaborate with software, platform, product, and data/ML teams on model-serving infrastructure, APIs, background processing, and data pipelines. Apply DevOps and SRE practices to reduce operational toil and safely integrate AI/ML capabilities into production.

Design, build, and maintain scalable ML infrastructure, data pipelines, and model serving for batch and real-time systems. Optimize compute resources and monitoring, implement MLOps and CI/CD, collaborate with ML scientists, research new technologies, and mentor junior engineers.

Lead architecture, build, and operate end-to-end ML infrastructure for training, deployment, and serving. Drive ML Ops practices, CI/CD, observability, scalable data pipelines, model serving, and reliability. Partner with ML scientists, mentor engineers, and evaluate technologies to deliver production-grade ML systems at scale.

Design, build, and operate scalable services and distributed systems supporting agentic AI capabilities. Lead system, API, and data architecture; integrate AI/ML solutions across development and production environments; establish standards for reliability, security, testing, observability, and compliance; partner cross-functionally on technical direction; and mentor engineers while building reusable AI tools, orchestration layers, and safeguards.

Design, build, and operate scalable backend and ML-enabled services for Expedia's advertiser platform. Productionize ML/LLM features (recommendations, ranking, forecasting, RAG), own APIs, data/feature pipelines, monitoring, and lead cross-functional delivery and engineering best practices.