Top AI & Machine Learning Jobs

Build and productionize internal AI applications from business-unit prototypes. Responsibilities include auditing AI tools, refactoring applications for security and scalability, integrating LLMs with Salesforce and the enterprise data lake, maintaining deployed solutions, managing stakeholders, and enforcing data governance, access controls, and hallucination mitigation.

Designs, develops, and deploys agentic AI solutions and process automation for Samsung care operations. Responsibilities include multi-agent workflows, MCP servers, enterprise API integrations, databases and vector stores, low-code automation, cloud environments, CI/CD, monitoring, process analysis, and AI governance. The role leads use cases from proof of concept through production, collaborates with business stakeholders and Korea HQ, and communicates technical implementations to nontechnical audiences.

Own the enterprise AI technical strategy, including model, vendor, platform, architecture, cost, security, and reliability evaluations. Establish AI standards, reference architectures, governance processes, and usage and spend visibility. Advise executives and engineering teams, assess AI risks, and partner with Risk, Compliance, and Legal. Prototype and validate LLM, API, agent, and RAG capabilities through working code, scaling productive experiments into reliable organizational capabilities.

Hands-on machine vision applications engineer responsible for lab testing, camera and optical setup, proof-of-concept builds, technical reporting, technology evaluation, customer-site troubleshooting, and deployment support. The role also includes image collection, labeling, model training, model evaluation, camera calibration, hardware evaluation, and building internal test resources. Candidates need machine vision fundamentals, AI vision familiarity, troubleshooting ability, and comfort working with hardware, industrial automation, and manufacturing customers.

Build and ship AI tools from prototype to adoption across partner communities. Own technical architecture, build-versus-partner decisions, privacy-safe deployments, testing, bias control, governance, and scale strategy. Manage technology and funding partnerships, pursue AI program opportunities, develop go-to-market budgets, and present recommendations to executive and board audiences. Translate tools into practitioner documentation and training while ensuring human oversight and data dignity.

Develops and deploys AI and machine learning models, algorithms, and analytics for federal customers. Responsibilities include analyzing datasets, testing and refining models, developing statistical and graph-based algorithms, creating visualizations, automating manual analysis, and integrating prototype solutions into production analyst workflows. Requires quantitative education, extensive data analysis and programming experience, and a TS/SCI clearance with polygraph.

Own end-to-end data science projects, including customer discovery, statistical analysis, predictive modeling, Python and SQL development, Databricks pipelines, AI-enabled analytics, quality checks, and customer presentations. Build reusable data assets at claims-data scale and contribute to Bonfire’s AI-powered products. The role requires rigorous analysis, strong communication, customer empathy, and comfort working with ambiguous problems.

Design, build, deploy, and operate secure, scalable AI-enabled products and internal tools. Evaluate models and services, develop prompts and context strategies, establish evaluations, monitoring, safeguards, and reusable architectures, and guide responsible AI adoption. Partner with product and engineering teams to prioritize opportunities, balance value and risk, mentor colleagues, and improve AI systems using production data and stakeholder feedback.

Develop and support AI-powered applications, automation solutions, and agent-based systems for business operations. Responsibilities include software development, integrations, Azure AI configuration, RAG and prompt engineering, testing, architecture participation, monitoring, troubleshooting, documentation, security collaboration, code reviews, and user enablement. The role works cross-functionally with engineers, stakeholders, legal teams, data professionals, and technology partners to deliver scalable and reliable AI solutions.

Design, build, evaluate, and deploy scalable AI models for insurance business problems. Analyze large datasets, validate models, monitor performance, and collaborate with data scientists and engineers to align solutions with business objectives and communicate results to non-technical stakeholders.