Top AI Engineer Jobs

Leads enterprise AI engineering strategy, architecture, platform development, and production deployment across AbbVie. Builds secure, scalable AI services integrated with AWS and enterprise systems; establishes MLOps, testing, monitoring, governance, validation, compliance, and responsible AI practices. Partners with technical, scientific, regulatory, legal, and business stakeholders, evaluates emerging technologies, guides proofs of concept into production, mentors engineers, and advances engineering maturity.

Lead design, build, and deploy of large-scale data platforms for LLMs, RAG, and agentic AI systems. Hands-on coding, architecting fault-tolerant pipelines, establishing MLOps/DataOps best practices, mentoring engineers, and operationalizing research into production across Exabyte-scale distributed systems.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

Lead hands-on engineer designing and modernizing cloud-native, reliable, and automated software solutions. Drive adoption of enterprise AI-assisted engineering practices, build automation frameworks, support performance and resiliency testing, lead code reviews, and communicate technical strategy and reliability standards across teams.

Lead enterprise deployment, governance, and operations for Microsoft Copilot and Power Platform across M365. Manage tenant controls, licensing/credit governance, AI risk/compliance, Microsoft vendor relationships, SharePoint/OneDrive content readiness, and analytics/reporting. Partner with architects, Security, Legal, Procurement, and business stakeholders to ensure secure, compliant, and adopted AI platform operations.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Lead and manage a software engineering team to design, build, test, and deploy AI-enabled applications (LLM integrations, RAG, APIs). Own project planning, budgeting, CI/CD, code quality, observability, microservices, and cross-functional collaboration while coaching and developing team members.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

Lead full-stack web development on GCP to build AI-powered search and discovery experiences. Design and implement web apps, services, and indexing pipelines, optimize performance, automate CI/CD and testing, mentor engineers, collaborate with product and ML partners, and productionize search/recommendation capabilities.

Design and build scalable AI platform capabilities to enable multi-step GenAI workflows, model execution, and integrations. Implement backend services, execution infrastructure, APIs, and observability to support production GenAI experiences across sales and product teams. Collaborate with AI engineers, data scientists, and product partners to turn AI use cases into reliable, extensible platform features.

Lead and mentor a data engineering team to design, build, test, and deploy cloud-based, AI-enhanced risk management data solutions. Collaborate across Agile teams and product managers, implement big-data and streaming systems, perform unit tests and code reviews, and drive technology adoption and performance tuning.

Lead a team building cloud-native, full-stack microservices and AI-enhanced risk management solutions. Design, deliver, and mentor on scalable AWS-based systems using modern web stacks and generative AI tooling to meet regulatory and business needs.

Develop and deploy generative AI and LLM solutions on Google Cloud Vertex AI. Build data pipelines integrating structured and unstructured data, implement prompt engineering and RAG, and connect AI capabilities to dashboards and real-time APIs. Translate business needs into technical solutions, stay current with generative AI advances, and communicate analytical results to technical and non-technical audiences.

Build and deploy production-quality AI/LLM agents, internal tools, and automated workflows to boost Solutions Architecture productivity; collaborate with SAs and cross-functional teams, harden prototypes into scalable systems, and maintain backend systems and demo assets.

Lead enterprise AI initiatives as a principal engineer and solution architect. Design, prototype, and deploy scalable generative AI, RAG, agentic AI, and predictive solutions. Translate business needs into technical requirements, evaluate technologies and vendors, conduct architecture reviews, and establish AI engineering, trust, safety, monitoring, and compliance standards. Mentor engineers and guide responsible AI practices across customer-facing products and internal operations.

Leads enterprise AI strategy, architecture, and production implementation across corporate functions, contact centers, and GTM products. Designs generative and agentic AI solutions, conversational and voice agents, RAG systems, intelligent workflows, and multi-agent orchestration on Azure and Google Cloud. Establishes AI governance, observability, SecOps, escalation, and operational models while influencing executives, product teams, and engineering organizations. Mentors technical staff and drives measurable AI adoption and business outcomes.

Design, build, and deploy production-ready intelligent solutions using machine learning, deep learning, generative AI, and large language models. Develop RAG pipelines, integrate AI services, manage model deployment and MLOps workflows, and apply strong software engineering practices. Collaborate with engineering, product, and data teams in a distributed environment to create scalable, reliable, responsible AI-powered products.

The Senior Backend Engineer will design, build, and scale AI-driven operations, manage the product lifecycle, and implement AI technologies while ensuring data security and system performance.

Build and ship full-stack features for an AI agent platform, rapidly prototype and iterate with pilot customers, and work hands-on with LLMs, agents, voice AI, and modern AI coding tools. The role also expects product ownership, independent opinions, strong engineering fundamentals, comfort with ambiguity, and a demonstrated passion for building through projects, hackathons, or open source.

Own the go-to-market data infrastructure, integrating HubSpot, Dreamdata, advertising platforms, PostHog, and website data into BigQuery. Diagnose tracking and attribution discrepancies, configure multi-touch revenue attribution, and build executive dashboards covering channel performance and revenue. Develop ABM workflows using Clay, enrichment, behavioral signals, and AI personalization. Partner with sales and marketing to improve funnel visibility, campaign decisions, and outbound effectiveness.

Build and operate autonomous AI agents for go-to-market workflows including inbound, outbound, pipeline management, and customer engagement. Develop evaluation frameworks, observability, human oversight, and ROI measurement for production agents. Ship MCP servers, agent skills, and web applications integrating CRM and revenue systems. Set technical direction, architect shared codebases, collaborate with sellers and RevOps, and advance best practices for LLM agents and evaluations.

Design, build, deploy, and optimize production AI systems, including LLM integrations, RAG pipelines, embeddings, vector search, predictive models, and inference systems. Collaborate with product and engineering teams to translate business needs into scalable solutions for recommendations, risk scoring, forecasting, and workflow automation. Own data pipelines, monitoring, evaluation, security, privacy, documentation, and continuous model improvement across cloud environments.

Develop and deploy AI-enabled applications for global supply chain and sourcing workflows. Translate ambiguous business needs into production-ready applications, pipelines, ontology objects, validation logic, and technical documentation. Build and test solutions using AI agents and LLMs, troubleshoot data and system issues, support user acceptance and production validation, and partner with business stakeholders, data teams, product owners, and external technical resources to deliver maintainable enterprise systems.

Develop and deploy AI-enabled applications for global supply chain and sourcing workflows. Translate ambiguous business needs into production-ready applications, data pipelines, ontology objects, validation logic, and technical documentation. Build and troubleshoot AI agents and LLM solutions, integrate data and models, validate application behavior with stakeholders, support user acceptance testing, and resolve post-deployment issues. Collaborate with product owners, subject matter experts, data teams, and external technical partners to deliver scalable, maintainable solutions in a regulated environment.