Top Artificial Intelligence Jobs

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Designs, tests, governs, and documents AI-assisted workflows for vulnerability and risk analysis in a federal cybersecurity environment. Responsibilities include structured prompt engineering, model parameter testing, hallucination reduction, prompt library management, quality validation, reproducibility benchmarking, and converting CISA cybersecurity workflows into standardized AI templates using enterprise Microsoft Copilot tools.

Designs end-to-end cloud, data, machine learning, deep learning, generative AI, and agent-based architectures for client engagements. Leads architecture reviews, technical governance, production readiness, security, compliance, scalability, reliability, and cost optimization. Partners with senior stakeholders, supports pre-sales scoping and proposals, mentors consultants and engineers, and develops AI reference architectures and internal accelerators.

Develop production software using AI-first engineering practices. Translate requirements into prompts, implementation plans, and acceptance criteria; generate and validate AI-assisted code; identify hallucinations, security issues, and logic gaps; create automated tests; and ensure architecture, performance, documentation, and production readiness. Build reusable prompt workflows, collaborate with product and technical teams, document decisions, and guide engineers in adopting AI-native development practices.

Leads Snorkel AI’s expert supply growth function, developing acquisition strategy, channel mix, partnerships, referral programs, BPO relationships, funnel optimization, analytics, and operating systems. Owns targets for acquiring, activating, and retaining specialized experts across domains and geographies. Builds and manages a high-performing team while partnering cross-functionally to improve quality, speed, cost, and marketplace capacity.

Build and deploy production-grade AI solutions that improve advisor effectiveness and automate business workflows. Responsibilities include full-stack and backend engineering, LLM and generative AI implementation, prompt engineering, data platform development, distributed systems, cloud-native architecture, Kubernetes deployments, security, scalability, and cross-functional initiative leadership from concept through production.

Owns a SaaS product area from market discovery through roadmap delivery, adoption, retention, and revenue outcomes. The role requires AI-native product management, customer research, KPI ownership, rapid prototyping, cross-functional alignment, and product strategy. Responsibilities include mentoring Product Owners and Product Managers, improving Agile practices, translating customer needs into initiatives, and partnering with engineering, sales, marketing, customer success, and leadership.

Graduate AI/ML engineering intern developing intelligent tools, predictive models, workflows, and prototypes for footwear and apparel design, simulation, and product innovation. Responsibilities include training models with simulated and experimental data, applying AI/ML to engineering workflows, building interactive internal tools, evaluating outputs, presenting findings, and documenting work for broader adoption.

The role involves designing and developing high-performance web apps, improving user interfaces, maintaining component libraries, and ensuring code quality through comprehensive testing and collaboration with other teams.

Own deployment and adoption of ARPA-H's AI assistant (GRACE): design agent/multi-agent workflows and prompts, train and onboard program teams, build reusable templates and playbooks, track usage and adoption, gather feedback, and translate needs into product requirements.

Designs, deploys, and operationalizes scalable AI microservices, MLOps workflows, production APIs, and ETL/ELT pipelines in secure cloud environments. Responsibilities include model monitoring, drift detection, automated retraining, data orchestration, CI/CD, ATO accreditation, architecture documentation, and stakeholder communication. The role supports Department of War data initiatives that improve situational awareness, operational readiness, and mission execution.

Partner with enterprise customers to design, prototype, and deploy production Generative AI applications using OpenAI APIs. Provide technical guidance, build prototypes, design enterprise architectures, manage stakeholder relationships, scale best practices, contribute developer resources, and relay product feedback to internal teams.

Leads enterprise architecture, AWS cloud transformation, application modernization, and Agentic AI platform initiatives. Designs hybrid cloud, microservices, distributed systems, multi-tenant SaaS, APIs, event-driven architectures, CI/CD, DevSecOps, and Infrastructure-as-Code solutions. Establishes architecture governance, security, compliance, and observability practices while collaborating with executives and engineering teams. Provides technical leadership, conducts design reviews and workshops, mentors delivery teams, and guides successful execution of transformation programs.

Build and deploy AI and machine learning solutions for clients, moving rapidly from prototypes to scalable production systems. Responsibilities include generative AI, AI agents, retrieval-augmented generation, model evaluation, enterprise integration, software engineering, cloud deployment, testing, monitoring, and communicating technical recommendations to business and technical stakeholders.

Supports enterprise AI governance through system intake reviews, risk and control analysis, scorecard maintenance, framework research, inventory management, reporting, AI literacy, stakeholder workshops, documentation, and governance workflow improvements. The intern gains experience with responsible AI, NIST AI Risk Management Framework concepts, ISO/IEC 42001, Microsoft Copilot, AI adoption, and governance operations.

Builds developer and customer relationships across the Jetson ecosystem, guides partners through embedded Linux and Jetson software integration, promotes NVIDIA AI libraries and edge AI capabilities, supports production deployments, and communicates developer feedback to engineering and product teams to shape platform roadmaps, reference designs, positioning, and adoption.

Design, build, and maintain infrastructure software for large-scale AI training and inference. Improve efficiency, resiliency, availability, observability, and reliability across distributed AI systems. Develop APIs and tools, analyze failures from application through hardware levels, define reliability metrics, and support NVIDIA AI platforms. The role requires expertise in distributed systems, monitoring, debugging, software engineering practices, and AI infrastructure, with preferred experience in large clusters, RDMA stacks, telemetry, and deep learning frameworks.

Design and build production AI, data analytics, and cloud solutions for product engineering. Own end-to-end architecture, agentic systems, evaluation standards, data pipelines, AWS platforms, APIs, and user interfaces. Develop full-stack applications, establish technical standards, mentor engineers, manage developers, and partner with engineering teams to move prototypes into adopted production systems. The role requires hands-on Python, SQL, modern front-end, LLM orchestration, RAG, data engineering, cloud, and cost-aware architecture expertise.

Build and maintain production-grade automation for real-world AI evaluations. The role owns end-to-end workflow orchestration, environment setup, artifact capture, reproducible datasets, scorecards, diagnostics, and release-triggered testing. It also develops agent- and LLM-driven workflows to evaluate customer use cases at scale, converting manual experiments into reliable standing protocols and actionable findings.

Conduct applied AI/ML and optimization research for modeling, simulation, and analysis capabilities. Develop surrogate models, software prototypes, and evaluation methods using Python, C/C++, PyTorch, and TensorFlow. Collaborate with research teams and customers, document technical results, present findings, and contribute to proposals and publications. Maintain reproducible Linux-based development environments using Git, Docker, and Kubernetes. U.S. citizenship and eligibility for a Secret clearance are required, with approximately 25% travel to Basking Ridge, New Jersey.

Provides strategic and technical leadership for enterprise AI, machine learning, generative AI, and advanced analytics. Establishes AI roadmaps, standards, governance, and responsible AI practices; oversees model, agent, platform, and data solution development; and leads MLOps, infrastructure, optimization, and lifecycle management. Partners with executives and business leaders to deliver measurable outcomes, manages budgets and vendors, and builds high-performing AI/ML and analytics teams.

Own and improve Clipboard’s internal operations tooling ecosystem, including workflows, integrations, automations, support platforms, and AI-driven systems. Translate operational needs into technical solutions, partner with engineering, troubleshoot production issues, improve system reliability, and execute tooling initiatives that increase operational efficiency and scalability.

Develop AI/ML and LLM solutions by building ETL pipelines, curating evaluation data, implementing machine learning algorithms, and improving scalable infrastructure. The role collaborates with researchers and developers, supports NLP and predictive analytics, applies Agile methodologies, and may lead interdisciplinary teams. Work includes data engineering, cloud and containerized deployment, GenAI operations, and algorithm implementation across varied customer missions.

Leads product strategy and execution for AI infrastructure and inference offerings, including GPU clusters, model deployment, serverless inference, and cloud platforms. Owns portfolio revenue, adoption, and customer satisfaction; partners with engineering, SRE, and go-to-market teams on delivery, pricing, and positioning. Builds and manages a high-performing product organization while representing the company with customers, partners, and analysts.

Leads governance, planning, execution support, reporting, and cross-functional coordination for CentralSquare’s enterprise AI initiative portfolio. Maintains initiative inventories, milestones, KPIs, dashboards, risk tracking, and escalation processes. Translates AI strategy into executable plans, coordinates dependencies across business functions, supports AI Council activities, and prepares executive reporting on progress, adoption, ROI, and outcomes.