Top AI & Machine Learning Jobs

Leads and develops an engineering team building, deploying, and operating machine-learning models and services for fraud prevention. Owns roadmap delivery, technical decisions, reliability, testing, monitoring, and stakeholder communication. Partners with data scientists, ML engineers, product, risk, and engineering teams. Drives responsible AI-tool adoption, recruits and coaches engineers, manages priorities, and ensures scalable, high-quality production systems.

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.

Design, develop, and maintain enterprise AI applications, including conversational assistants, copilots, autonomous agents, RAG solutions, and workflow automation. Build scalable APIs and microservices integrating foundation models and enterprise systems. Apply prompt and context engineering, memory management, observability, governance, and performance optimization. Collaborate with cross-functional teams to identify automation opportunities and participate in Agile development, code reviews, CI/CD, and DevSecOps.

Designs and leads human-machine integration experiments; develops data-processing procedures, algorithms, simulation content, and machine-learning models. Operates advanced laboratory equipment, analyzes human and systems data, validates and deploys models, and translates findings into product insights. Collaborates with engineers, human factors specialists, stakeholders, and customers to resolve performance issues and support evaluations. Work includes hands-on laboratory activities, equipment operation, military user evaluations, and potential international travel.

Design and improve production AI workflows for legal document intelligence using agentic orchestration, prompt engineering, retrieval, and model post-training. Own LLM evaluation and benchmarking frameworks, structured outputs, self-hosted model serving, inference optimization, and customer deployments. Build reliable, efficient, explainable systems using Python and modern LLM tooling, with emphasis on quality, latency, cost, and reproducibility.

Build and improve Swayable’s AI and analytics engine using Python, scientific computing, machine learning, and distributed systems. Develop scalable, high-performance software, optimize parallel computing architecture, productize AI and data analysis capabilities, and collaborate with engineers, data scientists, and researchers. The role also includes designing new features, maintaining testable code, mentoring junior engineers, documenting decisions, and providing technical feedback.

Build Fountain Forward’s agent-native AI product from prototype to production. Design multi-agent orchestration, tool-use interfaces, RAG systems, Databricks data models, ETL pipelines, and machine learning services. Deploy and monitor models using MLOps practices, MLflow, and CI/CD. Partner with product, data, and client-facing teams to translate business needs into reliable AI capabilities while maintaining clean code, documentation, testing, data quality, and explainability.

Leads the company’s AI governance program by developing frameworks, policies, controls, inventories, approval processes, and monitoring for AI and LLM tools. Partners with security, engineering, IT operations, risk, legal, and compliance teams to manage AI risks, third-party due diligence, data protection, regulatory compliance, audits, training, and responsible AI adoption. Tracks emerging regulations and standards while measuring program effectiveness and driving continuous improvement.

Design, develop, and apply AI and machine learning models for mission-critical defense applications. Build multimodal sensor, computer vision, NLP, and large language model solutions using Python and ML frameworks. Analyze large datasets, implement computational models and algorithms, collaborate with software engineers in agile teams, transition research into production-ready software, support proposals and technical reports, and present work to customers and professional audiences.

Build and deploy production-grade AI applications using Python and Azure. Develop LLM, RAG, and agentic workflows; integrate enterprise platforms, APIs, databases, and Microsoft 365; and create scalable backend services. Partner with stakeholders to translate ambiguous business needs into production solutions while applying strong testing, security, observability, scalability, and performance practices.