Top AI Engineer Jobs

Citizen Health seeks AI/Machine Learning Engineers to develop AI solutions for healthcare using advanced ML techniques, focusing on rare diseases and patient care. Responsibilities include designing ML models, optimizing LLMs, and ensuring compliance.

Own finance data infrastructure, system integrations, and AI automation for FP&A and the monthly close. Design and maintain EPM architecture, BigQuery/dbt pipelines, iPaaS integrations, data governance, and automated close/forecasting pipelines. Build AI/LLM-driven agents with human-in-the-loop controls to ensure SOX-compliant, reliable outputs.

Lead Software & AI Engineer responsible for designing, developing, and deploying software solutions integrating AI/ML. Requires extensive experience in cloud-native development and DevSecOps.

Senior technical leader responsible for the success of complex enterprise AI deployments: setting architecture and evaluation strategy, ensuring production reliability, coaching and developing engineering leads, partnering with customer executives, debugging critical issues, and scaling pilots to enterprise deployments while using AI-native engineering practices.

Design, build, deploy, and operate production AI systems for customers. Implement end-to-end AI workflows (models, prompts, agents, retrieval, evaluators), write maintainable Python services, monitor and debug live systems, create evaluation and observability tooling, and work closely with stakeholders to iterate systems for reliability and business value.

Build and own an agentic creative pipeline that generates, vets, and optimizes thousands of image and video ad variants daily: extend LLM critic feedback to multimodal assets, build fast cost-efficient video pipelines, route generative models by cost/quality, train and deploy brand-style checkpoints, implement policy pre-flight checks, and ship event-triggered ad pipelines with A/B validation in production.

Lead technical delivery and architecture of production AI systems for enterprise customers. Mentor engineers, integrate models/agents with customer data and APIs, operate and improve live systems, measure and debug failures, and drive pragmatic, reliable deployments that deliver business outcomes.

Build, train, test, deploy, and maintain AI models (LLMs, RAG, agentic workflows). Collaborate with engineers and product owners to implement MLOps/DevSecOps, cloud data foundations, event-driven integrations, and secure AI-enabled applications to support Air Force mission needs.

Design, train, test, deploy, and maintain AI/ML models and agentic workflows (LLMs, RAG). Build secure cloud data foundations on AWS, implement MLOps/DevSecOps (CI/CD, containerization, automated testing), design event-driven integrations, and collaborate with cross-functional teams to deliver AI-enabled solutions for Air Force mission needs. Secret clearance required.

Design, train, test, deploy, and maintain AI/ML models and agentic workflows. Build secure cloud data foundations (AWS), implement MLOps/DevSecOps (CI/CD, containers, automated testing), design event-driven integrations, and collaborate with engineers, data scientists, and product owners to deliver AI-enabled mission solutions for the Air Force. Requires Secret clearance and a relevant bachelor’s degree.

Lead and manage an onsite Edge AI Applications Lab to design, fabricate, and deploy tiny-ML models on microcontrollers and embedded platforms. Oversee lab projects, hardware prototypes, sensor data collection, model development, and cross-functional/customer collaborations. Mentor junior engineers, manage procurement and scheduling, and optimize real-time edge solutions for consumer, industrial, and automotive applications.

Design, develop, and deploy AI/ML models and production systems. Collaborate with cross-functional teams, mentor engineers, and ensure model performance, scalability, and reliability. Drive technical strategy for AI solutions.

The Principal Applied AI Engineer will lead the design of AI systems for finance, focusing on predictive modeling, intelligent automation, and software engineering best practices. They will oversee AI initiatives, mentor technical teams, and ensure compliance with financial standards and practices.

Design, develop, and deploy ML and deep-learning solutions (including generative AI). Lead end-to-end model lifecycle, build RAG/agentic workflows, integrate models into production, collaborate with engineering and clients, monitor performance, and document experiments and data provenance.

Lead development of an agentic AI platform: build agent orchestration, RAG/CAG pipelines, embeddings and vector-store ops, integrate LLMs (Anthropic Claude primary), deliver full-stack features across Python/Node/React, own production deployments, evaluation pipelines, and mentor a small senior engineering team while working hybrid in Denver.

As a Senior Consultant, you will design and develop AI solutions using Anthropic models, support client engagements, and lead automation efforts while collaborating with delivery teams.

Design, build, test, deploy, and operate AI-first full stack applications and Custom AI Agents. Integrate LLMs, embeddings, search, RAG, and MCP-based interoperability. Own features end-to-end, ensure reliability, security, observability, and lifecycle support in cloud-native, containerized environments.

Design, build, and productionize agentic LLM workflows to improve clinical data extraction quality and reliability. Integrate AI capabilities into production pipelines, ensure scalability and cost-effectiveness, develop evaluation/monitoring for safe deployment in regulated environments, and lead technical design and delivery with cross-team collaboration.

The Senior AI Engineer builds complex backend AI modules, optimizes retrieval systems, manages deployments, and develops internal tools at Cadre AI.

Lead design, development, testing, and deployment of production-grade agentic AI systems. Architect integrations with enterprise systems, drive advanced analytics and experimentation, mentor engineers, and own model lifecycle, reliability, and innovation for agentic/autonomous solutions.

Serve as a senior technical advisor to evaluate and implement intelligent automation, AI/ML, LLMs, NLP, IDP, and RPA to modernize DoD cybersecurity RMF/A&A workflows. Analyze processes, evaluate COTS/SaaS solutions, support vendor POCs, develop automation strategies and roadmaps, integrate with enterprise systems (ServiceNow, Power Platform, APIs), and produce executive briefings to support government decision-making.

Design and build scalable, performant data ingestion systems that handle variable healthcare data formats using AI. Partner with product and engineering leaders, implement observability and CI/CD, mentor engineers, and deliver secure, maintainable solutions that accelerate onboarding of new data feeds.

Build production-grade AI features and LLM pipelines (RAG, prompt engineering, context management) for financial-data workflows. Implement agentic workflows with Agno, optimize for accuracy/latency/cost, evaluate output quality, and integrate AI capabilities with backend APIs while maintaining code quality, observability, and testability.

Design, build, and deploy ML/DL and generative AI models and agentic workflows. Develop scalable ML pipelines, integrate vector databases and LLM orchestration, apply RAG/fine-tuning/RLHF, and implement MLOps practices for production-grade AI in cloud environments (GCP/AWS/Snowflake). Collaborate cross-functionally and maintain responsible AI documentation and monitoring.

Lead technical design and delivery of AI-native workflows for iOS/Android mobile engineering. Build and scale agentic AI-assisted development tools, spec-driven practices, CI/CD, testing, and release automation while partnering with product, design, security, and platform teams to improve quality and velocity.