Top AI & Machine Learning Jobs

Lead enterprise AI governance strategy and engineering to translate policy into automated, auditable controls and platforms. Build governance-as-code, embed controls in SDLC/MDLC, drive adoption, reporting, and audit readiness across risk, legal, compliance, and technology.

Define and drive the product vision, roadmap, and backlog for an AI-powered campaign analytics platform. Translate campaign measurement needs into epics, requirements, and success metrics; prioritize high-value use cases; ensure data and integration readiness; design explainable AI user experiences; and lead Agile delivery with cross-functional partners to deliver measurable marketing outcomes.

Design and develop deep learning NLP frameworks and LLM-based solutions (RAG, prompt engineering, tool calling, agent orchestration). Build evaluation/test setups, monitor and improve model performance, analyze financial texts for insights, present results to stakeholders, and collaborate with engineering/product teams to deploy scalable production solutions.

Lead development of AI‑first analytics and data products, modernize the data mesh, build autonomous human‑in‑the‑loop agents for schema drift and governance, design scalable batch and real‑time pipelines, enforce lineage/DQ/governance, and provide technical leadership across analytics, platform, risk, and domain teams.

Design, build, and maintain cloud-native ETL/data pipelines and integrations using AWS Glue, Java or Python. Deploy and optimize containerized applications on Amazon ECS, maintain CI/CD (Jenkins, Docker, Git), and support production incidents. Analyze large datasets to inform business decisions and collaborate with stakeholders to translate requirements into technical solutions.

Lead design and build of a global AI/ML deployment platform: architect control plane, multi-region deployments, APIs, autoscaling, and integrations. Own technical direction, engage clients, drive governance of AI-assisted development, and ensure resilient, secure production-grade infrastructure.

Lead design, development, and deployment of LLM-powered document digitization solutions. Manage full ML lifecycle, build scalable pipelines, provision cloud infrastructure, ensure security and compliance, monitor performance, mentor engineers, and drive continuous improvement and observability for production systems.

Lead architecture and delivery of production-grade agentic AI and NLP solutions for client onboarding. Design reusable frameworks (RAG, multi-agent orchestration, LLMs), integrate agents into enterprise systems, define safety/guardrails, advise stakeholders, and champion AI-assisted SDLC improvements to improve delivery, quality, and operational outcomes.

Lead Principal Investment Risk data strategy, governance, and data-product delivery. Design and operationalize applied AI/ML (LLMs, agents, traditional ML), ensure data quality, lineage, controls, verification/validation, and drive end-to-end production adoption with cross-functional stakeholders.

Lead a hands-on engineering team to design and deliver IoT and edge-to-cloud solutions that integrate AI/ML for real-time workplace insights. Architect resilient, secure pipelines, enforce data governance (FAIR), drive agentic AI adoption across the SDLC, mentor engineering talent, and split time between strategic leadership and active coding.