Top Artificial Intelligence Jobs

Serve as a pre-sales technical advisor for state and local government agencies adopting Claude. Partner with sales, product, and engineering teams to understand requirements, design scalable AI architectures, support evaluations and deployments, develop performance frameworks, and communicate technical concepts to executives and engineering teams. The role also contributes integration insights to product teams, supports Claude-based development, and requires frequent customer-site travel.

Designs and implements production automation systems, integrations, and AI-agent pipelines across business functions. Evaluates build-versus-buy decisions, establishes standards for testing, monitoring, documentation, and exception handling, improves system reliability, and measures operational value. The role also documents architectures, supports maintainability, and reviews other builders’ work.

Build and deploy production GenAI and LLM systems for financial services and healthcare clients. Responsibilities include understanding business operations, designing AI solutions, developing evaluation suites, delivering cloud-native systems on AWS, supporting enterprise AI adoption, contributing to reusable blueprints, and engaging senior stakeholders in pre-sales and solution scoping. The role requires autonomous ownership from problem framing through production deployment, with hands-on work involving Claude, Claude Code, Cowork, integrations, workflows, and connectors.

Build and productionize governed AI applications using Claude, Gemini, RAG, embeddings, vector stores, skills, plug-ins, and MCP. Establish enterprise standards for AI assets, access control, evaluation, monitoring, logging, and reliability. Partner with business, security, cloud, data, and engineering teams to deliver production-ready capabilities in a regulated environment, while providing support and documenting technical decisions.

Own AI-enabled products and capabilities from concept through launch, adoption, and continuous iteration. Translate strategy into requirements, user stories, acceptance criteria, backlogs, roadmaps, and delivery plans. Lead discovery, prioritization, stakeholder alignment, business case development, executive communications, demos, launch readiness, and continuous improvement across Product, Design, Research, Engineering, Enablement, and consulting teams.

Develop and maintain production AI/ML applications centered on agent-based workflows, LLM integration, and RAG pipelines. Build Python REST APIs, integrate external systems and AI frameworks, support full-stack application development, process structured and unstructured data, refine Postgres schemas, and optimize applications for reliability, performance, usability, and scalability. The role requires an active TS/SCI clearance with polygraph and full-time onsite work.

Leads enterprise-wide AI architecture strategy across the Generative AI platform stack. Defines reference architectures, cloud standards, integration patterns, governance controls, and reusable capabilities for RAG, model serving, agentic workflows, and AI applications. Partners with executives, engineering, security, risk, data, and operations teams to deliver secure, resilient, compliant, scalable platforms. Chairs architecture governance, directs modernization and build-versus-buy decisions, and mentors architects and engineering leaders.

Lead the architecture, engineering, and delivery of autonomous network capabilities across a 5G Mobile Core. Design intent-driven, agentic AI, closed-loop, self-healing, and observability systems; develop LLM agents, automation frameworks, digital twins, and AI/ML production architectures. Establish security, governance, rollback, and audit controls while troubleshooting network issues. Influence suppliers and industry standards, drive cross-functional initiatives, and mentor engineering teams adopting AI-driven network operations.

Build and maintain data ingestion, transformation, and ETL/ELT pipelines; support data modeling, warehousing, quality validation, governance, cloud platform deployment, and analytics environments. Collaborate with analysts, data scientists, and product teams, participate in Agile ceremonies and code reviews, and document data flows, lineage, and standards.

Design, build, and operate production multi-agent AI systems and automation for Marketing Operations. Integrate LLMs with business data (BigQuery, CRMs, Slack), build MCP servers, APIs, and scalable serverless/containerized services, implement observability and governance, and partner with GTM teams to deliver measurable automation and self-service workflows.

Manage and modernize legal operations through AI and legal technology implementation, process automation, matter tracking, Power BI reporting, and project coordination. Administer legal systems, develop dashboards, assess CLM readiness, maintain knowledge resources, drive tool adoption, and improve contract and leasing workflows. Partner with attorneys, paralegals, IT, and business teams to manage priorities, resolve bottlenecks, communicate status, and keep projects on schedule.

Design, implement, and train reinforcement learning and multi-agent algorithms; build scalable Python-based training pipelines and simulated environments; run distributed training with cloud and container orchestration; transition models to production, optimize system performance, and collaborate with domain experts to align rewards and evaluations with mission objectives.

Design, train, test, deploy, monitor, and maintain ML models (including LLMs/generative AI) for national security missions. Collaborate with data engineers, software developers, data scientists, and stakeholders to apply ML algorithms, triage large datasets, and deliver production-safe, mission-critical AI/ML solutions.

Owns the architecture and evolution of an enterprise AI platform supporting agent orchestration, retrieval systems, production LLM applications, governance, evaluation, safety, and multi-region cloud operations. Leads platform strategy, reliability, observability, incident response, security, cost management, and engineering standards. Mentors engineers and partners across product, engineering, data, security, and delivery teams to deploy scalable, measurable, enterprise-grade AI solutions.

Lead technical pre-sales engagements for Microsoft 365 Copilot, Copilot Studio, AI agents, and cloud endpoint solutions. Deliver demonstrations, discovery sessions, whiteboarding, solution designs, and proof-of-concept support. Develop technical win strategies, address objections and blockers, collaborate with partners and internal teams, educate customers, and guide adoption while embedding governance, security, and compliance into proposed solutions.

Leads the strategy, prioritization, governance, and execution of an enterprise AI transformation portfolio. Aligns senior stakeholders across Customer Success, Product, Technology, and other teams; develops roadmaps, investment recommendations, and decision materials; mobilizes cross-functional initiatives; resolves barriers; monitors risks and progress; and manages selected technology and consulting partnerships.

Design, prototype, and productionize Generative AI and LLM features across backend and frontend. Integrate ML models, fine-tune, perform prompt engineering, operate inference platforms, and support AI-based product launches while participating in on-call duties.

Administers and optimizes AI-enabled customer care platforms, including Salesforce, Cresta, and related generative AI capabilities. Translates operational needs into workflows, requirements, prompts, configurations, test scenarios, and acceptance criteria. Performs business administration, AI output validation, UAT, documentation, release support, training enablement, monitoring, and continuous improvement while coordinating with stakeholders, vendors, and technology teams on escalated issues.

Leads technical pre-sales engagements for enterprise AI workforce solutions. Develops win strategies, architecture designs, demonstrations, whiteboarding sessions, and proof of concepts. Advises customers on secure, scalable Microsoft solutions, coordinates stakeholders, removes technical blockers, mentors engineers, and provides product feedback. Expertise is required in either Microsoft 365 Copilot or Microsoft Cloud Endpoint technologies.

Build and operate shared AI platform capabilities, including SDKs, APIs, agent infrastructure, durable workflows, model integrations, retrieval, evaluation, and debugging tools. The role focuses on reliability, observability, performance, secure customer-data handling, and turning AI experiments into reusable production capabilities. Responsibilities also include partnering with product teams, driving adoption, diagnosing AI failures, and mentoring or leading broader technical initiatives.

Evaluate AI outputs related to insurance workflows, annotate healthcare documents, research payer requirements, and translate them into clear workflows and decision trees. Execute AI configuration changes, maintain process documentation, identify automation gaps, and track quality metrics for prior authorization, eligibility verification, and benefits checks. Collaborate remotely with healthcare operations teams to improve accuracy and reduce human intervention.

Design, deploy, and operate production-grade agentic AI systems, including multi-agent orchestration, RAG pipelines, LLM integrations, evaluation harnesses, observability, and cost and safety monitoring. Collaborate directly with client development teams, lead technical design sessions, establish reusable AI engineering patterns, and measure system performance across accuracy, latency, safety, and cost.

Owns end-to-end customer lifecycle programs across Latin America, including audience definition, onboarding, activation, adoption, and retention journeys. Builds AI-powered CRM automations, runs lifecycle experiments, analyzes customer and product data, and improves campaign performance against activation, retention, churn, and product adoption metrics. Collaborates with Product, Data, and Customer Happiness teams to ensure accurate tracking, compliant messaging, and reliable campaign execution.

Leads enterprise technology, architecture, and AI strategy across transformation initiatives. Defines technical roadmaps, architectural standards, governance frameworks, reference architectures, and responsible AI practices. Evaluates emerging technologies, drives proofs of concept and modernization, aligns investments with business priorities, and accelerates AI adoption. Partners with executives and cross-functional leaders, influences technical direction without direct authority, mentors senior engineers and architects, and represents the organization as a technology and AI thought leader.

Design and implement an AI agent that enables scientists to query ImmGen biomedical datasets and analytical tools using natural language. Integrate existing data resources, APIs, workflows, and visualization tools; develop interfaces, schemas, guardrails, and orchestration logic; and prevent hallucinations, invalid comparisons, unsupported biological claims, and costly computations. Collaborate with immunologists, computational scientists, and data analysts to define use cases, answer formats, and scientifically appropriate outputs.