Top AI & Machine Learning Jobs

Partner with business teams to identify high-leverage AI opportunities; design, build, and ship agentic AI solutions and internal AI platform components. Deliver production LLM-based agents, integrations, and CI/CD pipelines while driving AI enablement, safety, and adoption across the company.

The AI Engineer will develop production-grade AI tools, bridge business needs with technical solutions, and mentor other engineers while ensuring scalability and security in AI integration.

Lead quality assurance for geospatial imagery annotation across EO, SAR, and commercial sources. Mentor junior annotators, validate bounding-box/segmentation/point labels, identify systemic issues, write SOPs, and improve annotation tools and pipelines to support downstream AI/ML model needs.

Design and automate geospatial data ingestion and analysis pipelines using Python and AWS; apply geospatial data science to extract mission-relevant insights; create maps and visualizations for varied audiences; maintain repeatable workflows, documentation, and data integrity; gather customer requirements and implement solutions on-site.

Design, build, and deploy production-grade generative AI, RAG, and agentic solutions on AWS. Implement secure Guardrails, low-latency REST APIs, and MLOps pipelines (CI/CD, containerization, monitoring) to support real-time inference, model fine-tuning, and enterprise integrations that optimize network performance and customer experience.

Engineer serverless AWS utilities and integrate Generative/Agentic AI into QA automation. Build APIs, prototypes, and CI/CD-backed tooling to accelerate test coverage, improve fault detection, and collaborate with global engineering teams to deploy reliable automation solutions.

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.

Design, build, and operate scalable ML systems and services end-to-end: data sourcing, feature engineering, model development, deployment, monitoring, and operational support. Collaborate across product, data, and engineering, drive ML best practices, and mentor other engineers to improve model reliability, performance, and observability.

Lead research, design, and production deployment of large-scale search and recommendation ML systems. Architect multi-modal retrieval and multi-stage ranking pipelines, advance intent understanding and personalization, develop sequential recommenders, design offline/online evaluation (A/B, counterfactual), collaborate with engineering/product teams, and mentor scientists to deliver production-ready AI solutions.

Lead software and ML engineering for scalable, secure production systems. Author and review code, drive AI-assisted engineering practices, design ML pipelines and deployment/monitoring, automate remediation, evaluate vendors, and mentor teams on responsible AI and MLOps.

Design and build backend Python services, React/TypeScript dashboards, and data pipelines (Databricks/Snowflake). Integrate and govern LLMs and AI-assisted development tools, establish prompt engineering best practices, ensure reliability, security, and compliance, and coach teams on responsible AI use across the SDLC.

Lead design and production of generative and agentic AI solutions, build ML pipelines and MLOps, define enterprise semantic modeling and ontology governance, implement RAG and responsible AI practices, and mentor engineers while partnering with stakeholders to deliver measurable business outcomes.

Design, ship, and iterate production algorithmic systems for healthcare problems (ML, optimization, heuristics, or hybrid). Frame objectives, define metrics, validate solutions, fine-tune LLM products, and monitor outcomes while collaborating with stakeholders.

Design, develop, and productionize algorithmic systems (ML, optimization, heuristics, LLMs) for healthcare decisions. Own end-to-end solutions: problem framing, objective definition, model selection, shipping, evaluation, and monitoring to improve real-world outcomes and business metrics.

Lead AI strategy for the NetCloud platform, owning roadmaps for AIOps, LLM assistants, agentic workflows, MCP/API extensibility, and cross-functional alignment to ship AI-driven features that improve reliability, automate remediation, and deliver measurable customer value.

Lead client engagements to assess business challenges, define enterprise data and AI strategies, develop roadmaps, guide solution design and delivery, provide technical leadership and mentoring, and support practice growth via presales and thought leadership.

Develop and apply valuation analytics and statistical models in marketing, supporting decision-making and budget optimization through advanced statistical techniques and data analysis.

Lead an engineering data science team to define, develop, and deploy predictive and prescriptive analytics, ML algorithms, LLM solutions, and visualizations. Manage staff, suppliers, and stakeholder relationships while executing technical strategies and process improvements for product and manufacturing development.