Top AI & Machine Learning Jobs

Develop and operationalize Python-based geospatial data science solutions for DoD and Intelligence Community missions. Integrate structured and unstructured data through APIs and enterprise repositories, build analytic pipelines, dashboards, visualizations, and interfaces, and modernize legacy workflows. Analyze large datasets, create automated processes, document systems, identify patterns, and advise clients as a senior subject matter expert in high-consequence environments.

Assists with researching, prototyping, testing, documenting, and implementing AI-driven solutions. Supports analysis of business processes, data preparation, automation opportunities, software enhancements, and AI use-case development while collaborating with software developers, business teams, and technical stakeholders.

Designs and implements AWS cloud architectures integrating modernization, data engineering, generative AI, and machine learning. Leads client architecture sessions, presales engagements, cloud migrations, data pipeline and lakehouse development, governance, security, and multi-region deployments. Advises clients on scalable, secure, cost-optimized solutions while collaborating with sales and delivery teams. Requires extensive AWS experience, AI/ML and data architecture expertise, infrastructure-as-code proficiency, and strong customer-facing skills.

Develop predictive and statistical models, analytical datasets, data products, and AI-enabled workflows across marketing, ticketing, revenue, digital, and customer analytics. Analyze complex datasets, identify trends and data quality issues, validate AI-generated outputs, and translate business questions into scalable analytical solutions. Communicate technical findings and recommendations to technical and non-technical stakeholders.

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.