Top AI & Machine Learning Jobs

Designs, builds, tests, and deploys AI-powered tools and applications supporting manufacturing and business operations. Develops quality-control and production-automation systems, MLOps and model-evaluation pipelines, data pipelines, APIs, and integrations. Partners with business and technical teams, coordinates infrastructure needs, conducts system audits, creates documentation, troubleshoots issues, and improves AI/ML capabilities within an Agile/Scrum environment.

Designs, develops, tests, deploys, and supports machine learning and data solutions. Responsibilities include model training and optimization, scalable software development, MLOps implementation, CI/CD pipelines, automated testing, monitoring, documentation, production support, incident management, security compliance, and collaboration with technical and business teams.

Develop machine learning applications and AI technologies for industrial asset performance monitoring. Collaborate with data scientists, engineers, customers, and product managers; evaluate emerging technologies; create scalable data-driven models; and contribute research, innovation strategies, and technical thought leadership for AspenTech software platforms.

Own the evaluation, selection, optimization, and production deployment of large language models powering legal and medical document workflows. Build EvalOps and monitoring frameworks, benchmark model quality, cost, speed, and resilience, manage model transitions, optimize RAG and orchestration pipelines, and reduce hallucinations. Partner with product and go-to-market stakeholders while independently implementing changes across staging and production.

Lead Dwellci AI’s technical vision, engineering organization, AI model development, and scalable 3D generative design platform. The CTO will establish architecture and engineering practices, build and mentor an AI team, guide product innovation, collaborate with founders and designers, engage investors, and advance generative AI, computer vision, and computational design capabilities for architecture.

Develop and deploy data science solutions for financial crime and fraud prevention. Responsibilities include understanding, cleaning, validating, and preprocessing client data; engineering features; tuning machine-learning models; optimizing runtime performance; communicating findings; collaborating with client stakeholders and internal teams; and improving data science processes, tooling, and best practices.

Owns the strategy, architecture, prototyping, and delivery of enterprise AI and automation solutions. Partners with business and technical leaders to identify opportunities, design agentic workflows and integrations, evaluate AI platforms, ensure security and governance alignment, and guide operational rollout. The role also supports product planning, facilitates ideation workshops, advises executives, and co-leads AI enablement and training programs to drive organizational adoption.

Develop and maintain advanced data processing, entity resolution, and data cleansing pipelines using Python and SQL. Responsibilities include NLP, information extraction, data validation, query optimization, testing, deployment, monitoring, code reviews, and maintaining data integrity, privacy, security, and reproducibility. The role requires expertise in record linkage, named entity recognition, text processing, matching strategies, and machine learning libraries, with on-site work at SSA headquarters in Woodlawn, Maryland.

Build and operate secure, scalable AI/ML platform capabilities on AWS. Develop automation, infrastructure-as-code, CI/CD pipelines, AI model deployment and lifecycle services, generative and agentic AI integrations, data connections, governance controls, monitoring, and reliability capabilities. Collaborate with data scientists, engineers, architects, security teams, and stakeholders while evaluating emerging AI technologies and contributing to reusable platform standards.

Designs and operates enterprise AI platforms and cloud deployment architectures for supply chain and digital products. Responsibilities include building AI/ML and GenAI applications, APIs, data products, model-serving systems, RAG and agentic workflows, CI/CD, MLflow lifecycles, observability, governance, and validated GxP releases. The role also establishes engineering standards, integrates SAP data, improves reliability and cost efficiency, and mentors engineers while partnering with product, cybersecurity, quality, validation, and infrastructure teams.