Top AI & Machine Learning Jobs

Develop and maintain production AI and software solutions for alternative asset management workflows. Embed with business teams to identify use cases, build self-service AI capabilities, and deliver agentic workflows, retrieval pipelines, and evaluation tools. Use Python, SQL, Git, CI/CD, Docker, Terraform, and cloud deployment practices. Transition proofs of concept into production, lead concurrent projects, train technical and business users, and collaborate with stakeholders to define requirements and scope.

Participates in designing and developing generative AI solutions using language models, RAG, NLP, NLU, computer vision, and machine learning. Learns to fine-tune and train models, analyze and transform data, evaluate results, and apply AI to improve business processes. Collaborates with technical teams and internal business and IT partners on AI development projects while following industry best practices.

Build and maintain computer vision and machine learning pipelines for automated device inspection, grading, and quality assessment. Responsibilities include data preparation, feature engineering, model training and evaluation, production Python development, monitoring, annotation workflows, documentation, code reviews, and collaboration with product and operations teams. The role supports the full ML lifecycle and offers increasing ownership under senior engineering guidance.

Build and maintain factory data pipelines, structure production datasets, analyze manufacturing performance, develop dashboards and metrics, and prototype statistical or machine learning solutions. Collaborate with operations, quality, engineering, and program management teams to turn business questions into measurable analyses. Contribute clean, tested, documented code, identify data quality issues, and help govern factory data.

Build and integrate LLM-based agents, coding tools, model infrastructure, and role-specific AI interfaces for engineers, quants, and traders. Develop inference, configuration, integrations, tracing, and access controls; improve system reliability and performance; prototype applications for research, simulation, and trading; and collaborate with users to prioritize solutions.

Leads enterprise AI strategy, portfolio management, governance, lifecycle oversight, financial management, and value measurement. Advises teams on AI technology selection, including foundation models, agents, RAG architectures, orchestration platforms, and evaluation tools. Establishes responsible AI processes, approval workflows, model governance frameworks, executive dashboards, and investment prioritization methods. Partners with FinOps, Procurement, Enterprise Architecture, Technology, and business leaders to optimize AI spending, manage risk, drive responsible adoption, and realize measurable business value through influence and cross-functional leadership.

Design, build, secure, and operate production agentic AI applications integrated with enterprise systems of record. Lead multifunctional pods from prototype to production, establish reusable architecture and governance patterns, ensure AI-ready data in Microsoft Fabric, and make independent technical decisions. Develop reliable, scalable, observable systems with safe write-back, enterprise API integrations, cloud infrastructure, agent orchestration, retrieval-augmented generation, and rapid AI-assisted iteration.

Develop and improve analytical and machine learning models supporting product quality, risk outcomes, and business decisions. Responsibilities include statistical analysis, feature development, data exploration, predictive modeling, SQL-based analysis of large datasets, algorithm research, and workflow documentation. The role collaborates with data scientists, product managers, data engineers, and analysts in a product-focused fintech environment.

Build and scale AI agents, tools, infrastructure, and governance systems supporting digital twin workflows for clinical development. Collaborate with research, development, and delivery scientists to create AI-centric solutions, gather user feedback, drive adoption, communicate capabilities, and monitor advances in AI tooling. The role requires healthcare or clinical machine learning experience, AWS expertise, and demonstrated industry execution of AI projects.

Architect and build AI-driven workflow orchestration for autonomous cybersecurity operations. Responsibilities include automated incident response, LLM-based agent systems, event-driven Kafka pipelines, threat intelligence enrichment, multimodal data integration, real-time processing, and AI platform optimization using AWS Bedrock and Azure OpenAI. The role requires reliable, deterministic, auditable automation integrating security data, APIs, databases, and workflow systems.