Top Artificial Intelligence Jobs

As a Senior Scientist, you will develop machine learning methods for biologics design, optimize large-molecule therapeutics, and work collaboratively with experimental teams to enhance drug development processes.

Builds, operates, and scales secure backend systems for enterprise AI applications, including generative AI, RAG, and agent-based solutions. Develops Python services, APIs, ingestion pipelines, embedding and vector-search capabilities, integrations, and cloud deployments across AWS and Azure. Implements authentication, monitoring, logging, and reliability controls while troubleshooting performance, cost, and latency issues. Collaborates with architects, applied AI developers, data engineers, and infrastructure teams to deliver production-ready AI services.

Build and manage Microsoft Copilot Studio agents, Power Automate workflows, and low-code AI solutions for business teams. Translate business needs into technical designs, integrate approved data sources and APIs, test agent quality and security, and maintain deployments. Lead workshops, coaching, AI champion programs, and adoption efforts. Partner with HR, IT, business leaders, and Security on governance, access controls, ownership, monitoring, cost management, and continuous improvement.

Develops AI and data science capabilities for a Navy enterprise IT organization. Responsibilities include identifying AI use cases, creating roadmaps and risk assessments, building LLM agents and retrieval-augmented systems, developing data pipelines, evaluating AI outputs, and establishing governance and responsible AI practices. The role also requires stakeholder engagement, technical presentations, training, and delivery of prototypes in secure government environments.

Build and operate the AI platform powering enterprise agents and AI applications. Responsibilities include developing agent orchestration, retrieval pipelines, APIs, guardrails, evaluation frameworks, AI-assisted engineering workflows, cloud infrastructure, CI/CD, observability, and reliability processes. The role collaborates across engineering, product, analytics, delivery, and client teams to deploy secure, measurable, production-ready AI systems.

Lead hands-on delivery of production AI and agentic systems for US pharma and life sciences customers. Translate customer workflows into technical designs, build integrations with enterprise data and APIs, manage evaluation, testing, failure handling, deployment, and customer acceptance. Guide engineers, review code, coordinate with UK colleagues and Value Engineering, and develop reusable delivery practices. The role is customer-facing and requires regular onsite work rather than laboratory R&D or internal product development.

Designs, develops, tests, and delivers customized digital bots, generative AI bots, and agentic virtual agents for enterprise customers. Responsibilities include bot-flow architecture, routing application development, integrations, customer workshops, effort estimation, Agile leadership, testing, UAT and QA support, technical consulting, solution demonstrations, training, knowledge transfer, and leading delivery. The role requires extensive CCaaS, bot, webhook, IVR, speech recognition, and customer-facing consulting experience.

Own the enterprise AI architecture design authority for cybersecurity, including agentic frameworks, multi-agent orchestration, RAG, identity binding, least-privilege controls, and data fabric architecture. Lead three architects, govern AI reference designs and standards, chair design approvals, assess feasibility and risk, oversee vendor handoffs, and ensure architecture decisions remain auditable. The role also guides secure AI solutions across Azure and GCP, including LLM platforms, vector stores, and AI-driven security workflows.

Owns the commercial offer portfolio for Microsoft 365 Copilot, Cowork, and Microsoft 365 E7. Designs pricing, discounts, empowerments, governance, field activation, and performance measurement. Partners with Finance, Deal Desk, Business Planning, product, sales, and partner teams to improve offer ROI, simplify seller execution, resolve channel conflicts, automate reporting, and drive Copilot adoption, Cowork consumption, and E7 upsell across enterprise, commercial, public-sector, and education segments.

Owns the roadmap, backlog, prioritization, adoption, and delivery of AI, automation, enterprise data, data warehouse, and Power BI products. Partners with business stakeholders, technical teams, vendors, and leadership to define requirements, guide Agile delivery, support governance, validate solutions, and measure business value. Coordinates AI and automation pilots, data quality, reporting standards, user adoption, and continuous product improvement.

Develop and maintain web applications incorporating generative AI for a government customer. Build C#/.NET and JavaScript solutions, APIs, backend services, SQL integrations, and cloud deployments using AWS and Microsoft services. Integrate LLMs, AI platforms, MCP servers, and external systems; test, troubleshoot, document, and secure applications. Collaborate with analysts, UX developers, subject-matter experts, and technical teams to deliver practical AI-powered business solutions.

Develop and enhance secure GenAI web applications for a government customer. Integrate LLMs, AI services, APIs, MCP servers, tools, and business systems using C#/.NET, JavaScript, SQL, AWS, and Microsoft cloud services. Build web APIs and backend services, apply prompt engineering and RAG, troubleshoot complex issues, evaluate technical approaches, and maintain application quality, security, scalability, and documentation. Collaboration, code reviews, and support for cloud deployments are also required.

Own the strategy and roadmap for AI evaluation, quality, safety, and guardrails. Lead model and workflow assessments, benchmark development, testing requirements, production monitoring, and safeguard improvements. Partner with Engineering, Data Science, AI Platform, Security, Legal, Privacy, and domain experts to guide technical decisions, adoption, releases, and risk management. Communicate quality evidence and trade-offs to leaders, support platform adoption, evaluate build-versus-buy options, and mentor product managers.

Build and own the enterprise AI context and knowledge layer, including knowledge graphs, RAG, semantic search, connectors, APIs, and agent integrations. Design and evaluate AI agents, skills, prompts, and workflows; improve retrieval quality, reliability, security, and cost efficiency. Administer Slack, Google Workspace, Glean, and AI assistants while managing access controls, monitoring, incident response, documentation, and governance. Partner across business functions and security teams to deliver scalable, production-ready AI capabilities.

Leads the architecture, development, and deployment of enterprise-scale AI systems and platforms. Establishes technical direction, architecture standards, governance, monitoring, evaluation, and responsible AI practices. Drives AI-enabled modernization of legacy systems, including code transformation and service decomposition, while improving software development lifecycle productivity. Partners with engineering teams, influences enterprise technology decisions, and mentors technical staff. Requires deep expertise in distributed systems, large-scale architecture, and production AI/ML systems.

Designs and develops generative AI applications using large language models, retrieval-augmented generation, prompt workflows, structured outputs, and enterprise integrations. Responsibilities include building ingestion and vector retrieval pipelines, evaluating model quality and reliability, optimizing latency and cost, and implementing monitoring, logging, fallbacks, and feedback mechanisms. The role requires strong Python and API development skills, software delivery experience, and responsible AI practices, with preferred experience in orchestration tools, vector databases, cloud AI services, containerization, and scalable inference.

Build production AI applications using machine learning and foundation-model services. Develop Python-based data, inference, evaluation, and integration workflows; connect AI services to enterprise applications; assess quality, latency, cost, and reliability; and collaborate with product, data, and software engineering teams to deploy and improve AI features.

Leads enterprise AI platform deployments from architecture through adoption, reliability, and scale. Builds production AI workflows, agents, copilots, and automations; integrates LLMs with enterprise systems; and governs identity, data, security, privacy, and multi-system workflows. Advises executives on AI strategy, ROI, and adoption roadmaps, leads architecture sessions and technical workshops, manages engineering teams, and develops reusable deployment standards and accelerators.

Owns Fiserv’s data and AI portfolio across product, engineering, and go-to-market teams. The role prioritizes investments, manages pilot-to-production delivery for Customer and Enterprise AI, shapes reusable AI platform roadmaps, supports launches and strategic client engagements, manages partner dependencies, ensures responsible AI governance, reports portfolio performance, and leads portfolio managers and product owners.

Develop and deploy UiPath RPA solutions and generative AI applications using AWS Bedrock, LLMs, prompt engineering, fine-tuning, and RAG. Integrate AI with business applications and automation workflows, build secure APIs and cloud-native services, support data governance, and apply DoD responsible AI and RMF controls. The role also includes testing, documentation, architecture participation, production support, and mentoring junior developers.

Lead AI-driven transformation of the marketing design workflow. Responsibilities include developing Gen-AI strategies, automating Figma-based processes, setting creative direction, reviewing work, prioritizing the marketing design board, and building scalable templates, component libraries, brand pipelines, web components, and self-service asset systems. Partner with Marketing and Design leadership to coordinate launches, align brand and product work, and increase output without sacrificing quality.

Build, train, fine-tune, and evaluate language models with a strong focus on reinforcement learning. Design environments, reward functions, datasets, benchmarks, and evaluation frameworks; analyze data; manage reproducible experiments; diagnose training issues; and communicate findings to technical and non-technical stakeholders. The role also involves knowledge representation, collaboration with domain experts, and compliance with HIPAA and sensitive-data policies.

Serve as a strategic and operational force multiplier for the Edge CVP and leadership team. Translate priorities into execution plans, operating rhythms, decision forums, and accountability mechanisms. Lead talent planning, organizational health, executive communications, special projects, and cross-functional initiatives. Own OPEX, COGS, headcount planning, forecasting, investment tradeoffs, and spend tracking. Manage and develop business management and program management teams.

Owns AIX’s end-to-end learning strategy, including manager enablement, DRI and Atlas training, AI fluency, compliance training, and Aspire programming for early-career hires. Builds role-based curricula, learning content, champion and train-the-trainer networks, and measurement systems focused on behavior change, adoption, readiness, productivity, and data quality. Partners with HR, product teams, leaders, and subject matter experts across a distributed engineering organization.

Leads product definition and development for next-generation AI hardware supporting Azure training and inference workloads. Identifies customer needs, market opportunities, and competitive gaps; translates them into product requirements, roadmaps, investment proposals, and hardware strategies. Drives technical programs from concept through execution while collaborating with architecture, engineering, business teams, customers, and senior leadership. Requires expertise in AI systems, GPU architecture, networking, data centers, reliability, and cloud infrastructure.