Top AI & Machine Learning Jobs

Build and deploy spatial AI capabilities for generative home floor plans. Responsibilities include training and adapting ML models, creating APIs and evaluation pipelines, improving reproducibility, managing latency and cost, extending research into product features, partnering on MLOps and deployment, and mentoring engineers and researchers.

Develop and migrate reporting assets from SAP BusinessObjects to QlikView and Qlik Sense. Analyze and rebuild Web Intelligence, Crystal Reports, and universes; debug legacy and new reports; optimize SQL, scripts, expressions, data models, and load processes; validate output accuracy; document migration logic; and collaborate with stakeholders on requirements and report specifications.

Build and deploy AI and machine learning solutions for clients, moving rapidly from prototypes to scalable production systems. Responsibilities include generative AI, AI agents, retrieval-augmented generation, model evaluation, enterprise integration, software engineering, cloud deployment, testing, monitoring, and communicating technical recommendations to business and technical stakeholders.

Supports enterprise AI governance through system intake reviews, risk and control analysis, scorecard maintenance, framework research, inventory management, reporting, AI literacy, stakeholder workshops, documentation, and governance workflow improvements. The intern gains experience with responsible AI, NIST AI Risk Management Framework concepts, ISO/IEC 42001, Microsoft Copilot, AI adoption, and governance operations.

Analyze multimodal healthcare, genomic, clinical, and biomedical imaging data to support drug discovery, biomarker development, disease progression modeling, clinical trial design, and real-world evidence analyses. The role involves building statistical and machine learning models, developing data pipeline modules and interactive tools, collaborating with clinical, genomics, and biology teams, contributing to data strategy, and communicating findings through publications and stakeholder presentations.

Provides strategic and technical leadership for enterprise AI, machine learning, generative AI, and advanced analytics. Establishes AI roadmaps, standards, governance, and responsible AI practices; oversees model, agent, platform, and data solution development; and leads MLOps, infrastructure, optimization, and lifecycle management. Partners with executives and business leaders to deliver measurable outcomes, manages budgets and vendors, and builds high-performing AI/ML and analytics teams.

Leads technical foresight on how AI and emerging technologies may reshape global finance. Assesses AI capabilities, architectures, scaling, constraints, governance, failure modes, and systemic risks; translates findings into scenarios, hypotheses, briefings, research priorities, and thought leadership. Partners with research, standards, advocacy, and industry stakeholders to stress-test assumptions and evaluate technology’s implications for investment workflows, institutions, markets, professional roles, and standards.

Leads consumer personalization strategy and production development for recommendations, ranking, search, browse, carts, marketing, and agentic AI experiences. Builds recommender systems, learning-to-rank models, LLM agents, evaluation frameworks, and experimentation programs. Partners with Product and Engineering on deployment, monitoring, and model quality while mentoring senior data scientists, setting technical direction, shaping roadmaps, and communicating trade-offs to executives.

Build and operationalize production-grade image and video generative AI systems. Responsibilities include designing multimodal pipelines, agentic workflows, MCP servers, microservices, prompt management, model integrations, ML infrastructure, and scalable inference services. The role emphasizes reliability, cost, latency, observability, and collaboration with data scientists, designers, product managers, and creative teams to deliver AI-powered visual experiences.

Lead the architecture and development of MHC’s data platform, including ingestion, modeling, governance, pipelines, and feature stores. Build production AI and LLM capabilities such as document understanding, RAG, embeddings, evaluation, and intelligent product features. Manage and mentor a small data and AI engineering team, establish technical standards, define roadmaps, and partner with Product, Engineering, and business stakeholders.