Top Artificial Intelligence Jobs

Develop predictive machine learning models and generative AI applications using Azure, Python, pandas, and scikit-learn. Build and deploy production GenAI solutions, including RAG, hybrid search, evaluations, multi-agent systems, and user interfaces. Collaborate on dataset design, statistical modeling, process-control integration, and full-stack application development. Present findings to nontechnical audiences while addressing business problems across research, finance, manufacturing, legal, and environmental safety functions.

Research, architect, and ship AI-powered product capabilities using agents, LLMs, RAG, embeddings, and machine learning. Build scalable multi-tenant AI infrastructure, agentic chat systems, data integrations, observability, evaluation frameworks, and guardrails. Translate business needs into measurable user-facing solutions while collaborating with product, data science, engineering, and leadership teams on the AI roadmap.

Build and optimize Databricks Lakehouse architectures and scalable ETL/ELT pipelines using Spark and Delta Lake across AWS, Azure, and GCP. Tune performance and cloud costs, integrate MLflow for AI model operationalization, and implement CI/CD and Infrastructure-as-Code deployments. Enforce governance, security, and compliance through Unity Catalog. Partner with architects, data scientists, engineers, and clients to deliver production-ready AI solutions and documentation.

Designs and implements enterprise AI solutions and architectures using Python, data science, big data, cloud, and LLM technologies. Builds scalable and resilient AWS environments, deploys production applications, leads architecture and platform engineering teams, and addresses complex client business needs. Requires extensive technical expertise, TS/SCI clearance with polygraph, and the ability to work independently while mentoring others.

Lead a hands-on engineering team building agentic AI applications (agent orchestration, tool calling, RAG, LLM features). Own architecture, technical strategy, evaluation, observability, and AI safety guardrails. Hire, coach, and grow engineers while partnering with product, design, data, and business stakeholders to deliver measurable product outcomes.

Build the systems, automation, data models, and AI-enabled workflows supporting Sales and Customer Success. Responsibilities include creating GTM data infrastructure, value ledgers, account context tools, workflow discovery systems, pilot instrumentation, CRM automation, expansion signals, and data-quality observability. The role partners directly with GTM leadership and field teams to identify bottlenecks, prototype solutions, measure adoption, and scale effective processes.

Project-focused AI engineering internship embedded on a DevOps team. The intern will prototype generative assistants for alert response, build retrieval-augmented systems over permissioned internal data, design guarded agentic remediation workflows, and support internal engineering Q&A. Responsibilities include tool-use workflows, observability integration, evaluation, documentation, Git-based delivery, and strict controls for permissioning, auditing, logging, safety, and cost.

Design, develop, and deploy production-grade AI and machine learning solutions, including generative AI, LLM, RAG, and agentic applications. Build cloud-native systems on AWS, APIs, microservices, and reusable AI platforms. Establish practices for model evaluation, monitoring, security, governance, and responsible AI. Partner with business and technical teams to identify use cases, provide architectural guidance, mentor engineers, evaluate emerging technologies, and drive enterprise AI adoption.

Build and lead CVS Health’s enterprise AI FinOps capability, including governance, operating models, cost allocation, chargeback/showback, anomaly detection, reporting, and optimization. Evaluate AI financial-management tooling across cloud and third-party providers, translate AI unit economics into executive insights, and establish an AI FinOps Center of Excellence. Partner with Engineering, Finance, Product, Procurement, and executives to align AI investments with business value, accountability, scalability, and financial discipline.

Lead and coach a small Frontier AI Infrastructure engineering team while remaining hands-on with coding. Own the model inference layer, including scalable backend systems, networking, latency, usage metrics, integrations, incident response, and operational health. Set technical direction, manage delivery commitments, engage cloud vendors and government customers, support secure cloud-native deployments, and build engineering standards for testing, code review, on-call, and compliance.

Own technical SEO and organic search strategy across client accounts, including audits, keyword research, content optimization, schema markup, analytics reporting, and optimization for AI-driven search surfaces. Monitor performance using SEO tools, translate data into client recommendations, communicate directly with clients, and collaborate with account, web development, and content teams. The role is fully remote and requires U.S. work authorization without sponsorship.

Lead end-to-end product builds by orchestrating AI coding agents to create applications, automations, and agents. Translate customer needs into deployable solutions, instrument and iterate on outcomes, validate AI outputs for security and quality, engage with customers to find automation opportunities, and document prompts and patterns for the team.

Develops, evaluates, deploys, and optimizes AI and machine learning solutions for healthcare and business applications. Responsibilities include predictive analytics, natural language processing, image recognition, AI pipelines, clinical decision support, data preparation, model validation, compliance, documentation, stakeholder collaboration, staff training, vendor evaluation, and ongoing model monitoring. Provides limited rotational on-call and help desk support for healthcare data reporting needs.

Leads strategy, roadmap, and delivery for enterprise AI-powered process transformation. Oversees agentic systems, intelligent automation, orchestration frameworks, and production-scale AI platforms while ensuring security, explainability, governance, reliability, and measurable business impact. Partners with Product, Engineering, Data Science, Operations, Risk, Compliance, and senior executives to prioritize investments, drive adoption, manage cross-functional teams, and deliver productivity, cost, risk, and client-experience improvements.

Design, prototype, and productize AI-powered workflows and tools for Customer Success using LLMs. Partner with stakeholders to prioritize opportunities, integrate AI with internal systems and APIs, and enable teams through documentation, workshops, and best practices. Facilitate scaling prototypes to production with centralized AI engineering support.

Design and prototype AI-powered solutions for Customer Success using LLMs and automation. Translate business needs into roadmaps, build prompts/agents/ retrieval workflows, integrate APIs and internal data, enable teams with documentation and training, and partner with engineering to productionize scalable AI systems.

Partner with Customer Success leaders to identify and prioritize AI opportunities; prototype and productize LLM-powered solutions and end-to-end workflows; integrate AI with APIs and internal data sources; develop prompts, skills, agents, and retrieval systems; enable teams via documentation, workshops, and training; and collaborate with engineering to scale prototypes into production.

Supports AI governance and compliance by monitoring global regulations, developing compliance roadmaps, gathering evidence across product and software development lifecycles, maintaining policies and model documentation, and updating risk registers. Builds AI agents and automations, contributes to internal training, supports RFP responses, and coordinates public AI Trust Center content while collaborating with Product, Engineering, Security, Legal, Data Science, and Sales.

Assists with the development, testing, deployment, and optimization of generative AI and LLM-based applications. Supports prompt engineering, model fine-tuning, foundation model integration, cloud deployment, documentation, and business use-case implementation. Collaborates with product managers, software engineers, cloud architects, and stakeholders while researching emerging AI technologies and contributing to scalable, responsible AI solutions.

Assists with developing, testing, fine-tuning, and deploying generative AI and LLM-based applications. Supports prompt engineering, foundation model integration, cloud deployment, documentation, and model optimization. Collaborates with product managers, engineers, architects, and business stakeholders to design solutions for real-world use cases. Researches emerging AI technologies and contributes to responsible, scalable, cloud-based applications.

Own the strategy and roadmap for Retool’s AI platform infrastructure, enabling enterprises to deploy, operate, observe, secure, and scale AI-powered applications and agents. Partner with engineering and enterprise customers on deployment, reliability, access controls, APIs, infrastructure as code, observability, and operational tooling. Lead initiatives from discovery through launch and adoption while making technical tradeoffs and turning recurring customer needs into scalable platform capabilities.

Develops AI/ML prototypes, APIs, microservices, integrations, and lightweight user interfaces for federal and DoD-aligned mission workflows. The role integrates and optimizes machine learning models for dashboards and applications, supports data ingestion and preprocessing, validates performance, and helps transition models into production. Collaboration with data scientists, architects, developers, PMO leadership, and cybersecurity teams is required. The position requires five years of relevant experience, a bachelor’s degree, ITIL v4 Foundation certification, and Public Trust or active Secret clearance.

Designs AI/ML pilots, predictive analytics and anomaly detection models, behavioral analytics, content classification, personalization algorithms, and end-to-end machine learning pipelines. Develops prototypes and production-ready solutions, evaluates AI technologies, supports experimentation, integrates ML insights into dashboards, and ensures model governance, explainability, bias mitigation, accuracy, and security compliance in federal and DoD-aligned environments.

Build, deploy, and maintain AI agents and automated workflows across business functions. Integrate solutions with enterprise systems, monitor performance, manage AI infrastructure, and participate in governance. Analyze workflows, run pilots, measure ROI, establish risk controls, and support adoption through training and documentation. Partner with stakeholders across departments while maintaining privacy, security, and regulatory controls.

Designs enterprise AI architecture, agentic systems, real-time data pipelines, RAG platforms, multimodal AI integrations, and secure multi-cloud infrastructure. Leads adoption of LLMs, orchestration frameworks, vector databases, streaming systems, and AI-native developer tools. Partners with engineering, data, product, and security teams on governance, scalability, cost optimization, and responsible AI. Requires extensive software, data, cloud, and production AI experience.