Top AI Engineer Jobs

Design and implement secure AI-powered tooling and platforms using LLMs, RAG, agents, and MCP. Lead architecture, prompt evaluation, safety guardrails, backend integrations, quality gates, monitoring, and performance optimization. Deliver Python services, mentor engineers, conduct code reviews, coordinate cross-functional development, lead incident analysis, and translate business strategy into engineering roadmaps and measurable priorities.

Lead the technical strategy, development, operation, and accreditation of a secure agentic AI platform in AWS GovCloud IL5. Build Kubernetes and Docker environments, CI/CD pipelines, Infrastructure as Code, observability, security controls, and MLOps capabilities. Develop ATO and cATO artifacts, integrate managed AI services, support container hardening and secure delivery, and collaborate with security and authorization stakeholders. Mentor engineers and guide the platform roadmap for operational DoD environments.

Designs, trains, evaluates, and deploys AI/ML and deep learning models for mission-critical applications. Analyzes complex datasets, develops predictive algorithms, integrates models into operational systems, and supports testing, validation, deployment, and lifecycle management. Collaborates with engineers, data scientists, and mission stakeholders; documents AI/ML initiatives; researches emerging technologies; and may support LLM, generative AI, NLP, cloud, distributed computing, and MLOps capabilities.

Build and operate AI-driven customer acquisition infrastructure, including outbound workflows, enrichment pipelines, lead routing, personalization, audience scoring, campaign automation, experimentation, and attribution dashboards. Partner with demand generation, lifecycle marketing, data analytics, product marketing, marketing operations, and sales to translate growth objectives into scalable automated systems.

Build and operationalize AI-driven semantic solutions using ontologies, knowledge graphs, controlled vocabularies, APIs, MCP tools, agents, and reusable software components. Enable inference, search, normalization, entity resolution, and data integration across enterprise platforms. Collaborate with platform, product, data, ontology, and governance teams; embed FAIR data principles, responsible AI, security, compliance, documentation, and CI/CD practices into scalable services.

Develop AI agents that improve productivity across HR, Finance, Product Support, Customer Service, and CAD Design. Build and integrate Retrieval-Augmented Generation models, fine-tune open-source models, collaborate with cross-functional stakeholders, integrate AI into existing systems, and troubleshoot and optimize model performance.

Develop production-grade AI-enabled platform capabilities, APIs, backend services, integrations, and reusable software enhancements. Build model integrations, prompt and retrieval workflows, extraction logic, and agent configurations while ensuring scalability, security, reliability, testing, observability, and maintainability. Collaborate with Product, Solution Engineering, Application Engineering, Platform Reliability, and Architecture teams to translate requirements into customer-facing platform solutions. Contribute to CI/CD, automated testing, deployment, documentation, and responsible AI practices.

Lead technical discovery, design custom Cloudflare architectures, and deliver demos/PoCs. Own onboarding, adoption, and technical relationships across the customer lifecycle. Drive commercial expansion with quota responsibility, translate platform metrics into business outcomes, and advise stakeholders on security, networking, and cloud architecture best practices.

Design, implement, and optimize classical and deep-learning computer vision algorithms for ADAS. Tasks include object detection, segmentation, tracking, image enhancement, dataset analysis, model optimization for real-time embedded ECUs, bench testing, root-cause analysis, documentation, and cross-functional integration for serial automotive production.

The Lead AI Product Engineer will design, build, and operate AI agent systems, focusing on architecture, reliability, and performance while collaborating on user-centric workflows.

The Senior/Staff Backend Engineer will build AI-driven products, optimize performance, collaborate with teams, and establish best practices for Applied AI at Fora.

The Senior/Staff Full Stack Engineer will develop AI-driven products, enhance the advisor portal's functionality, and ensure system reliability using best practices in software and AI development.

Customer-facing senior solutions engineer for developer-led startups. Lead technical discovery, deliver demos and POCs, map customer AI/product initiatives to the Cloudflare Developer Platform, build technical champions, and evangelize platform capabilities across developer and executive audiences. Requires deep platform knowledge and pre-sales experience, with up to 50% travel.

Drive AI/ML solution sales for cable, media, and entertainment clients by developing POCs, proposing solutions, advising customers, building roadmaps, coordinating offshore delivery teams, and performing data-driven analysis to identify business improvements and implementation plans.

Lead AI Test & Evaluation programs that measure mission-level outcomes across models, subsystems, and user workflows. Architect T&E systems, advise government customers, translate findings into engineering recommendations, run program execution (schedule/budget/team), and grow business opportunities while leading a multidisciplinary technical team.

Lead technical direction for consumer-facing AI initiatives serving 45 million monthly visitors. Design, build, deploy, and operate full-stack agentic and LLM applications using Python, LangChain, LangGraph, and Langfuse. Guide AI infrastructure, evaluation, observability, scalability, and tooling decisions; establish engineering patterns and platform roadmaps; mentor engineers; collaborate with Product, UX, Analytics, and data teams; and document architectural decisions and best practices.

Lead requirements and systems engineering for an enterprise application integrating AI capabilities. Work with customers, developers, systems engineers, and domain experts to define requirements, create technical specifications and Jira stories, validate features, evaluate AI architectures and model performance, and coordinate AI solution delivery through Agile development. The role requires hands-on technical execution, functional testing, stakeholder communication, and a current Top Secret/SCI clearance with favorable polygraph.

Design and deliver scalable software, data engineering, and AI solutions for Barclays’ Liquid Financing business. Build and optimize cloud-based platforms using Python or Java, AWS, Kubernetes, event-driven architectures, and real-time data pipelines. Integrate LLM APIs, embeddings, and retrieval patterns into applications while collaborating with product managers, engineers, quants, and strats. Provide technical leadership, guide architecture and strategy, manage risk and controls, advise stakeholders, and mentor less experienced specialists.

Leads the design, development, and deployment of enterprise AI applications, intelligent agents, LLM platforms, MCP servers, RAG systems, and AI APIs. Manages AI platform capabilities including model routing, governance, observability, and orchestration. Leads and mentors engineers, shapes technical strategy, partners with business and clinical teams, evaluates emerging AI technologies, and presents recommendations to senior leadership. The role requires hands-on Python development and expertise in production AI systems within a regulated biotechnology environment.

Design, develop, test, and productize AI/ML algorithms. Responsibilities include selecting and analyzing data, training and improving models, evaluating system performance, integrating scalable solutions, validating data and model quality, and identifying distribution issues. The role requires software engineering expertise, machine learning deployment experience, and oversight of iterative verification and validation processes.

Design, prototype, deploy, and support generative and predictive AI solutions for U.S. Government customers. Develop interfaces and applications, agentic workflows, and LLM fine-tuning capabilities; research emerging AI techniques; translate technical concepts for customers and subject matter experts; and support pre-sales activities. The role requires Python development, AI/ML modeling, RAG technologies, LLM frameworks, APIs, and government-sector experience.

Leads architecture and development of a production agentic AI platform. Defines technical strategy, engineering standards, reliability and model evaluation practices, and scalable AI capabilities. Designs RAG integrations, third-party data connections, model routing, tool use, and code execution systems. Partners across Engineering, Product, and Data to drive adoption, resolve technical challenges, and align AI investments with customer outcomes while contributing directly to product delivery.

Provide technical leadership across engineering teams by defining architecture, building reusable cloud-native platforms, and designing distributed systems and production AI applications. Establish engineering standards for reliability, security, observability, governance, and operational excellence. Mentor senior engineers and technical leaders, influence architecture through collaboration, and drive modernization, AI-assisted development, and platform engineering initiatives.

Designs and deploys AI/HPC networking infrastructure for customers, including Linux GPU systems, network fabrics, rack layouts, automation, monitoring, and data center upgrades. Supports service reliability, performance optimization, and lifecycle operations. Partners with sales, engineering, support, and customers on solution design, requirements, deployments, product feedback, technical content, training, and roadmap planning. Requires strong data center networking, solution architecture, communication, and customer-facing skills, with up to 20% domestic and international travel.

Build, evaluate, integrate, and operationalize machine learning, deep learning, and generative AI capabilities for federal mission applications. Responsibilities include developing models and datasets, creating AI services and interfaces, integrating APIs and pipelines, deploying scalable and reproducible solutions, documenting workflows, and improving capabilities through technical and user feedback. The role collaborates with software and data engineers, technical leaders, subject-matter experts, and mission users in a controlled government environment.