Top Artificial Intelligence Jobs

Designs and implements AI-enabled workflows for cyber and intelligence missions, including LLM-based agents, multi-step automation, API and data-store integrations, and production deployments in classified environments. Collaborates with mission users, analysts, developers, and leadership to translate operational workflows into reliable, secure capabilities. Requires Python experience, TS/SCI clearance with polygraph, and relevant AI/ML or software engineering experience.

Own Pulley’s AI technical direction and production LLM systems for permitting intelligence. Build document extraction, classification, retrieval, and agentic workflows; establish evaluation and observability standards; make model, architecture, cost, and latency decisions; and mentor engineers. The role requires hands-on production experience with LLMs, AI coding agents, reliability engineering, and end-to-end ownership of AI domains, with possible work on OCR, document understanding, fine-tuning, and full-stack AI features.

Lead the engineering team building Chariot’s AI claims platform, including agent systems for case origination and claims matching, human-in-the-loop review, evaluation infrastructure, and scalable AI performance. The role owns product decisions, internal tooling, cost efficiency, and production reliability while recruiting and developing full-stack and ML engineers. Experience with production software, end-to-end product ownership, early-stage startups, and modern LLM systems is valued.

Leads the design and development of cloud-native platforms supporting generative AI agents. Responsibilities include building backend services, developer enablement tools, CI/CD pipelines, Kubernetes and multi-cloud infrastructure, platform security, observability, DORA metrics, and production engineering practices. The role requires mentoring, architectural leadership, cross-functional collaboration, and expertise in cloud-native systems, networking, infrastructure as code, and systems programming.

Leads complex Physical AI and software programs from concept through productization, deployment, and launch. Establishes roadmaps, execution processes, operating rhythms, and cross-functional alignment across AI, engineering, product, UX, finance, sales, and operations. Oversees program portfolios, budgets, forecasting, resources, risks, dependencies, and business outcomes while transitioning customer pilots and emerging technologies into scalable products.

Leads complex Physical AI and software programs from concept through customer deployment, productization, and launch. Establishes roadmaps, execution processes, prioritization, financial management, and operating rhythms while aligning global AI, engineering, product, UX, finance, sales, and operations teams. Drives pilots into scalable products and builds a high-performing technical program management organization.

Lead product strategy and roadmap for AI Ready Data capabilities—data ingestion, transformation, enrichment, synthetic data, and tooling for retrieval, fine-tuning, evaluation, and agent workflows. Write product specs, partner with engineering, research, design, and GTM, translate experiments into scalable features, work with customers to surface requirements, and define success metrics to monitor adoption and performance.

Executes data solution workstreams for client engagements, including requirements gathering, data analysis, modeling, reporting, dashboards, integration, testing, documentation, and validation. Translates business questions into usable data outputs, assesses data quality and delivery risks, supports client adoption and training, coordinates assigned workstreams, and escalates project dependencies while improving analytics delivery assets and processes.

Build and deploy production AI, machine learning, and computer vision systems for aircraft manufacturing. Responsibilities include developing AI-enabled services, APIs, data pipelines, visual inspection and anomaly detection solutions, model safeguards, monitoring, and integrations with factory systems and equipment. The role owns end-to-end delivery, leads technical design, mentors engineers, and establishes scalable practices for reliable AI deployment.

Lead presales engagements to uncover business drivers and design responsible, scalable AI solutions on the Microsoft cloud. Run enterprise discovery workshops, shape measurable outcomes, architect across Azure Data/AI/Security/Integration/Power Platform, produce SOWs and phased delivery plans, and communicate value to executive stakeholders while partnering with sales and delivery for deal readiness and risk mitigation.

Lead end-to-end post-training of open-weight language models on proprietary legal data, including dataset development, objective design, supervised fine-tuning, preference optimization, reinforcement learning, evaluation, and distributed training. Build production model-serving systems, agents, tooling, and infrastructure. Partner with product engineers, lawyers, and domain experts to translate workflows into model and evaluation strategies and deliver AI capabilities to users.

Build backend architecture and infrastructure for LLM-driven agent systems, including orchestration, tool integration, context management, evaluation, observability, and production deployment. Develop services connecting AI models with insurance data and platform APIs, define reliable AI-native architectural patterns, and collaborate with product, design, and engineering teams to deliver underwriting workflows.

Leads a three-person computer vision team developing, training, optimizing, and deploying custom YOLO object-detection models for live full-motion video. Architects secure data and training pipelines, establishes annotation standards, manages dataset challenges, and optimizes inference using GPU acceleration. Integrates computer vision systems with video and software platforms while coordinating Agile delivery, technical direction, metrics, and code standards. The role requires advanced Python, deep learning, video processing, model deployment, and team leadership experience.

Owns strategy, roadmap, lifecycle, metrics, responsible AI practices, commercialization, and go-to-market for AI/ML-powered healthcare products. Partners with Data Science, Engineering, Legal, Marketing, Sales, and customers to define use cases, requirements, success criteria, pricing, and launch plans. The role requires managing platform and partner integrations, coordinating cross-functional dependencies, ensuring HIPAA and privacy compliance, and translating technical AI concepts for business stakeholders.

Build, test, deploy, and govern AI workflows for M&A transactions using legal technology tools. Translate M&A legal reasoning into reliable AI logic, evaluate outputs, manage datasets and testing frameworks, advise on AI governance and professional responsibility, train attorneys, assess new technologies, and lead AI adoption across the practice.

Leads business intelligence, process improvement, automation, Power Platform, AI, and data initiatives. Partners with business leaders to improve productivity, develop dashboards and data models, implement enterprise data warehouses, deliver AI-powered solutions, and establish governance. Manages and mentors team members while driving measurable operational improvements, stakeholder adoption, and continuous innovation across manufacturing and business operations.

Lead and grow AI Platform and AI Enablement engineering teams building infrastructure, tooling, and integrations for AI features. Define AI engineering roadmap with product and data science partners, set standards for reliability, observability, security, and responsible AI, manage and develop engineers, engage in code/design reviews, and coordinate cross-team delivery with Platform and Data teams.

Leads AI security, systems engineering, platform modernization, and developer productivity initiatives. Responsibilities include managing AI vulnerability remediation, designing secure AI applications, modernizing Java platforms, building CI/CD and DevSecOps capabilities, enabling rapid security releases, developing automation, and establishing enterprise engineering standards. The role provides technical leadership across multiple teams while partnering with architecture, security, infrastructure, and development stakeholders.

Lead AI Transformation Engineer drives adoption of AI-assisted development across issuer solutions, sets engineering standards, conducts architecture/code reviews, pilots AI developer tools, modernizes mainframe-distributed integrations, builds reusable patterns and internal tooling, and mentors engineers to improve quality, reliability, and delivery throughput.

Owns strategy, roadmap, governance, release management, vendor relationships, adoption, and ROI for enterprise generative AI platforms. Partners with legal, technology, security, governance, and business teams to evaluate features, manage risk, develop workflows, coordinate deployments, and drive responsible adoption. Tracks platform performance and business value, supports users, manages releases and escalations, and provides executive recommendations on roadmap, investment, and AI enablement.

Deliver Microsoft Dynamics 365, Power Platform, Copilot, Fabric, and Azure solutions for clients. Build applications, automations, integrations, AI agents, and data analytics solutions using Power Apps, Power Automate, Copilot Studio, Fabric, Power BI, Dataverse, APIs, and Azure services. Translate business requirements into technical designs, document solutions, communicate with stakeholders, improve client processes, and travel nationally to customer locations as project needs require.

Drive revenue for Devs.ai by selling an enterprise AI platform through direct and channel sales motions. Responsibilities include quota attainment, partner enablement, co-selling, product demonstrations, consultative discovery, solution positioning, pipeline forecasting, and collaboration with account executives, solution engineers, product leaders, and enterprise stakeholders. The role requires translating AI capabilities into business value for technical and executive buyers.

Build node-based generative AI workflows for image, video, audio, and multimodal content pipelines. Create showcase demos, integrate and test AI models, design automation flows, develop user-friendly widgets, and collaborate with AI engineers, UX designers, and product leadership. The role requires prompt engineering, model chaining, workflow automation, and hands-on experience with systems such as ComfyUI or similar node-based platforms.

Own the end-to-end architecture of Bristol Myers Squibb’s enterprise agentic commercial orchestration capability. Define multi-agent patterns, decisioning, customer and journey state, channel integrations, evaluation, guardrails, observability, and secure enterprise architecture. Lead technical direction with an implementation partner while ensuring BMS ownership, portability, explainability, compliance, and operational sustainability across personalized HCP engagement journeys.

Builds and ships user-facing product experiences and integrations using low-code, no-code, and AI-assisted development tools. Translates stakeholder needs into maintainable solutions, integrates platform services and APIs, applies authentication, security, privacy, and compliance controls, and partners with Product, Architecture, DevOps, Security, and QA. The role also defines testing, telemetry, documentation, runbooks, acceptance criteria, and post-launch product health metrics.