Top AI & Machine Learning Jobs

Lead development of AI-powered patient experiences: LLM-driven intake and therapist matching, between-session support, evaluation and safety infrastructure, outcome measurement, and HIPAA-compliant product delivery. Partner with Engineering, ML, Data Science, Clinical, and Legal to ship and monitor models in production, write evals, design red-teaming and human-in-the-loop flows, and measure patient outcomes.

Lead execution of Responsible Use of AI (RUAI) approvals and AI risk oversight. Coordinate cross-functional reviews, assess and document AI risks, improve governance tools/workflows, and escalate material issues to leadership. Engage use case owners, vendors, and control partners to ensure compliance with AI regulatory and control frameworks.

Lead full-stack and AI-enabled software engineering for a treasury data platform. Build React/TypeScript UIs and Python backends, deliver ETL/data pipelines on Databricks/Snowflake, and establish governed LLM/agentic development practices to ensure reliability, reproducibility, and secure AI-assisted engineering across the team.

Lead design, build, and scale of generative and agentic AI solutions; deliver production ML systems, reusable platforms, observability and MLOps, mentor AI teams, partner with stakeholders to prioritize and measure enterprise impact.

Lead design, development, and production deployment of AI, GenAI, and LLM solutions. Build and tune models in Python, collaborate across product and engineering, drive MLOps adoption, ensure model governance and regulatory compliance, and communicate results to technical and business stakeholders.

Lead analytics strategy and decision support, perform deep-dive analyses, build predictive and ML models, design experiments, create trusted reporting and dashboards, ensure data quality, partner cross-functionally, and influence product, growth, revenue, operations, and finance through evidence-based recommendations.

Administer, secure, and optimize enterprise AI tools and LLM platforms. Manage access, SSO integrations, usage policies, cost monitoring, audits, and SOC 2 readiness. Build automation scripts, train employees, and partner with IT, Security, and Business teams to enforce AI governance and data protection.

Research, develop, and productize scalable AI/ML and GenAI solutions (LLMs, NLP, agentic systems). Build, evaluate, optimize, and deploy deep learning models, collaborate with engineers and product teams, publish research, and contribute to Conversational AI and data science roadmaps.

Design and prototype AI-native, agentic user experiences by delivering coded prototypes (React/TypeScript), leveraging LLMs and generative tools, and partnering with product and engineering to ship scalable, data-driven AI features and design system primitives across the platform.

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.

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.

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.