Top AI & Machine Learning Jobs

Own Data, Cloud & AI coverage using a proprietary dataset to produce forward-looking, data-driven research. Analyze trends across AI/ML operations, databases, data integration, governance and cloud infrastructure; produce client-facing insights, thought leadership, and collaborate with product and engineering to improve research methodology and platform fidelity.

Build and configure AI agents and ML models for healthcare finance customers using existing platform infrastructure. Partner with customers and product teams to scope solutions, create visualizations, present AI capabilities, validate model reliability, and establish testing rigor. Generalize customer-specific solutions into reusable platform features while shipping changes through code review and automated CI/CD.

Analyze microbiome and longitudinal clinical datasets, develop microbiome metrics and biomarkers using statistics and machine learning, create visualizations and reports, draft clinical research documents, support product and study development, and contribute to scientific publications and proposals.

Build and maintain end-to-end machine learning infrastructure, including data and training pipelines, deployment workflows, monitoring systems, and supporting platforms. Partner with researchers and ML practitioners to productionize models, improve reliability, scalability, observability, reproducibility, speed, and cost efficiency, and automate operational workflows. This engineering-focused role supports reliable production ML systems rather than research-first development.

Lead design and implementation of end-to-end AI/ML solutions with emphasis on LLM fine-tuning/RAG, MLOps, cloud platform evaluation and setup (Azure/Oracle), model deployment and monitoring, and integrating model outputs into business reports (PowerBI/Oracle Analytics). Document processes and collaborate with cross-functional teams.

Designs, develops, administers, and supports enterprise automation, AI agents, copilots, workflows, applications, dashboards, and integrations. Partners with stakeholders to identify automation opportunities, provides Tier 3 troubleshooting, manages governance and security, supports adoption and training, documents best practices, evaluates emerging AI technologies, and ensures responsible, scalable, cost-effective solutions across Microsoft platforms.

Build and scale an AI-powered audit workbench that transforms raw evidence into reviewable workpapers. Responsibilities include designing ingestion and extraction pipelines, deterministic recomputation and exception detection, workpaper generation, evaluation infrastructure, and frontend review tools. The role emphasizes production LLM reliability, golden-dataset evaluations, schema design, traceability, deterministic regulatory logic, structured human review, and end-to-end system ownership in a regulated domain.

Lead design and implementation of LLM-enabled analytics on Snowflake, build natural-language-to-SQL and conversational interfaces, develop semantic data models and prototypes, productionize AI solutions, set architectural best practices, mentor teams, and ensure responsible, governed AI usage.

Develop and integrate large-scale data applications using Accumulo, Hadoop, Kafka, Spark, Yarn, Python, and JavaScript. Implement predictive analytics, graph algorithms, and machine learning models; design client/server systems and integration points; improve development workflows and support analytic toolchains for Intelligence Community missions.

Provide expert coaching to professionals and business leaders on adopting and scaling AI: identify high-impact use cases, develop AI strategy, lead transformations, and deliver practical, actionable guidance via virtual coaching sessions.