Top Artificial Intelligence Jobs

Audit the growth-marketing tech stack to identify and prioritize AI opportunities, build and deploy AI-driven workflows inside tools (Qualified, 6sense, Outreach, etc.), measure and optimize performance, document implementations, and deliver a structured handoff. Work closely with Marketing Operations and centralized AI governance to ensure QA and maintainability.

Design, implement, and deliver agentic AI platform features across the full lifecycle to boost developer productivity. Advocate and dogfood AI-powered tooling, mentor engineers, experiment with agentic approaches, and collaborate with teams to scale robust, maintainable systems.

Lead global AI and automation transformation for Internal Control: prioritize and pilot AI-enabled controls, redesign workflows, drive continuous monitoring, partner with regional teams and data science, build roadmaps, dashboards, and business cases, ensure regulatory compliance, and scale adoption across the enterprise.

Lead editorial strategy, quality guidelines, and evaluation frameworks for AI-powered content across MSN and Microsoft surfaces. Drive cross-functional delivery with product, engineering, and design teams, focusing on model evaluation, prompt engineering, trust and safety, and scalable editorial operations to meet audience and business objectives.

Investigate product and UX friction using session recordings, support tickets, and analytics; prioritize fixes by business impact; prototype and ship solutions using AI tools; collaborate with product, engineering, and underwriting; measure outcomes and iterate.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready AI products.

Consult with clients to define data needs, analyze structured and unstructured data, build and deploy ML and LLM-based systems (including GenAI agents), design prompting strategies, operationalize models and segments for marketing use cases, and communicate insights through presentations, dashboards, and tools to support strategic decisions.

Client-facing data scientist working on compliance and regulatory use cases: define data needs, build pipelines, analyze structured and unstructured data, develop and deploy ML and LLM-based systems (GenAI), design prompting and guardrails, and deliver dashboards, models, and strategic recommendations to risk and compliance teams.

Hands-on GenAI consultant role designing, prototyping, fine-tuning, and deploying LLM-powered solutions (RAG, agents, prompt engineering). Support solution architecture, implement MLOps, evaluate models for safety and compliance, integrate with cloud systems, and collaborate with cross-functional teams while documenting methodologies and researching new GenAI techniques.

Support client compliance and risk teams by defining data needs, building data pipelines, analyzing structured and unstructured data, and deploying ML/GenAI solutions (LLMs, agents). Design, fine-tune, and productionize LLM-based systems, implement monitoring and dashboards, ensure regulatory compliance (GDPR, EU AI Act), and collaborate with engineering and product teams while contributing to internal training, publications, and business development.

Partner with client teams to define data needs, acquire and explore structured and unstructured data, build predictive models and LLM-based systems, develop GenAI applications, optimize prompting strategies, and deploy scalable AI solutions while communicating insights and advising on data-driven marketing strategies.

Work on end-to-end Generative AI projects: design, prototype, fine-tune, and deploy LLM-powered solutions (RAG, agents, prompt engineering). Support solution architecture, implement MLOps/CI-CD, evaluate model performance and compliance, and collaborate with cross-functional teams to produce secure, scalable AI applications.

Lead development and adoption of AI-driven and traditional test automation, maintain UI automation frameworks, own performance/load testing strategy, align testing to architecture and risk, mentor QA engineers, and collaborate with product and engineering to improve quality and testing efficiency.

Lead design, build, and deployment of AI-enabled analytics using Microsoft AI technologies and cloud data platforms. Develop AI assistants, copilots, RAG and agentic workflows, integrate solutions with Power BI and semantic models, prototype and productionize with Python and SQL, implement governance and observability, and partner with BI, data engineering, and business stakeholders to scale enterprise AI use cases.

Lead enterprise AI and data strategy, roadmap, governance and delivery to move proofs-of-concept into scalable, compliant production solutions. Build teams, platforms, reusable patterns, and adoption practices; partner with business, IT, security, legal and quality to ensure measurable value, regulatory alignment, and responsible AI practices.

The AI/EHRS Training Specialist designs and manages training programs on AI and EHRS tools, facilitating sessions and ensuring all employees effectively learn and apply these technologies in compliance with policies.

Support integration of LLMs and developer tools into engineering workflows, run internal AI enablement sessions, identify workflow bottlenecks and implement AI-driven automations, liaise between product managers and developers, and assist with sprint management and project tracking.

Define and validate AI-powered product capabilities by identifying emerging technologies, building prototypes, evaluating platforms, and designing scalable architectures (LLMs, RAG, semantic search, vector DBs). Architect AI agents and secure integrations across cloud platforms, develop reference architectures and reusable capabilities, and partner with product and engineering teams to deliver production-ready, governed AI solutions.

Design, build, and deploy AI/ML and Generative AI capabilities within enterprise integration and middleware platforms. Develop scalable APIs, microservices, and data pipelines; implement intelligent monitoring, anomaly detection, and predictive workflows. Collaborate with cross-functional teams, follow SDLC and deployment practices, provide production support, and participate in on-call rotations while documenting designs and troubleshooting issues.

Lead design and implementation of production agentic AI systems that integrate LLMs with Teak's platform APIs. Build RAG pipelines, MCP/tool integrations, evaluation frameworks, and AI infrastructure on cloud (AWS/GCP/Azure). Contribute backend services in Python, ensure compliance and safety, and operate systems in production including observability and on-call duties.

Design and implement an end-to-end generative AI video pipeline that converts lesson specs into inspected, versioned videos. Build agentic orchestration, programmatic animation, TTS narration, evaluation/judging systems, symbolic verification of worked examples, and scalable Python/TypeScript media infrastructure.

Lead and contribute to applied AI/ML projects across the SDLC: mentor teams, design and implement LLM-based solutions (RAG, copilots), architect integrations, manage data persistence choices, CI/CD, and present solutions to clients while influencing technical direction.

Lead and deliver Applied AI implementations across full SDLC: setup, requirements, design, development, testing and deployment. Manage risks, run stakeholder interviews and workshops, build client relationships, support PMO and change management, and contribute to mentorship, recruitment and internal practice growth.

Lead delivery of Applied AI projects across the SDLC: setup, requirements, design, development, testing and deployment. Facilitate executive workshops, manage client stakeholder alignment, identify and mitigate risks, drive organizational change management for adoption, mentor team members, contribute to recruitment and methodology improvements, and manage project planning, budgets, resources, and status reporting.

Lead delivery of applied AI implementations across full project lifecycle; run workshops and stakeholder engagements; manage risks, client relationships, PMOs and teams; drive change management and adoption; mentor staff and support business development and internal growth initiatives.