Top AI Engineer Jobs

The Senior AI Engineer will architect LLM agents, drive ML lifecycle processes, build GenAI solutions, and lead a data engineering team. Responsibilities include overseeing data architecture, scalable deployments, and mentoring.

Lead the architecture and development of a multi-RAT Open RAN digital twin supporting LTE and 5G NR simulation. Integrate production MAC and scheduler software with PHY, channel, UE, traffic, mobility, interference, and network models. Build fidelity tiers, AI/ML evaluation pipelines, reproducible experiments, calibration and validation practices, automated testing, and CI/CD workflows. Improve simulation scalability and reliability while collaborating across wireless, PHY, AI/ML, software, and lab-validation teams.

Design and build scalable backend services, data-processing workflows, APIs, databases, cloud infrastructure, and AI-powered systems. Lead architecture and technical strategy, own reliability and production operations, mentor engineers, and guide cross-team initiatives from design through deployment. The role focuses on transforming unstructured customer data into reliable structured outputs and developing LLM-powered and agentic workflows.

The role involves conducting research and developing generative AI technologies, creating prototypes, collaborating with partners, and driving innovation at American Express.

As an AI Engineer III, you'll develop agentic AI systems, build LLM-powered features, and operate AI systems in production while collaborating with cross-functional teams.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

As a founding ML research engineer, you'll design AI-powered engineering systems, implement ML algorithms, and integrate deep learning techniques while collaborating with experts in the field.

Lead architecture, development, training, validation, and testing of AI/ML/DL and computer vision models. Mentor and lead a small team, track milestones and releases, run technical reviews, practice agile and CI methodologies, and drive process and productivity improvements.

Lead design, development, training, validation, and deployment of AI/ML/CV/DL algorithms and models. Architect product features, mentor engineers, run technical reviews, track milestones, and drive agile CI-driven development and process improvements.

Design and build autonomous, enterprise-grounded agentic workflows using RAG and ESRE. Integrate and fine-tune LLMs with internal APIs and SaaS tools. Provision scalable cloud infrastructure via Terraform, implement CI/CD, secure network architectures, and deploy observability and tracing to monitor agent behavior. Provide technical leadership, documentation, and enable internal teams to leverage the Elastic Stack for automated business task completion.

The AI Engineer at BLEN will design and build agentic AI systems, including LLM-powered applications, integrate APIs, and ensure responsible AI practices for federal clients, focusing on performance and observability.

Designs and implements enterprise AI solutions and architectures using Python, data science, big data, cloud, and LLM technologies. Builds scalable and resilient AWS environments, deploys production applications, leads architecture and platform engineering teams, and addresses complex client business needs. Requires extensive technical expertise, TS/SCI clearance with polygraph, and the ability to work independently while mentoring others.

Lead end-to-end product builds by orchestrating AI coding agents to create applications, automations, and agents. Translate customer needs into deployable solutions, instrument and iterate on outcomes, validate AI outputs for security and quality, engage with customers to find automation opportunities, and document prompts and patterns for the team.

Develops AI/ML prototypes, APIs, microservices, integrations, and lightweight user interfaces for federal and DoD-aligned mission workflows. The role integrates and optimizes machine learning models for dashboards and applications, supports data ingestion and preprocessing, validates performance, and helps transition models into production. Collaboration with data scientists, architects, developers, PMO leadership, and cybersecurity teams is required. The position requires five years of relevant experience, a bachelor’s degree, ITIL v4 Foundation certification, and Public Trust or active Secret clearance.

Design, develop, deploy, and iterate production machine learning and generative AI solutions. Responsibilities include model development, NLP, prediction, anomaly detection, LLM and RAG integration, data pipelines, feature engineering, evaluation, monitoring, API integration, and architecture decisions. The role collaborates cross-functionally to balance accuracy, latency, cost, scalability, and maintainability.

Analyzes wafer fabrication, equipment, sensor, metrology, and manufacturing data using AI, machine learning, statistics, and data-mining techniques. Develops data pipelines, analytical tools, dashboards, deployed models, and monitoring methods for anomaly detection, yield prediction, excursion analysis, and root-cause investigation. Collaborates with engineering, manufacturing, MES, and AI/ML teams to translate results into actionable insights, support experiments, improve processes, and establish responsible AI and data-quality practices.

Develops and deploys AI and machine learning solutions for intelligence and cyber workflows in classified environments. Responsibilities include building LLM-powered workflows and agent-based automation, integrating AI into operational systems, developing data pipelines, evaluating models, optimizing workflows, and conducting operational testing. The role collaborates with mission users, analysts, software developers, and leadership to deliver reliable, scalable, secure, and production-ready AI capabilities.

Develop and deploy enterprise-scale AI and machine learning solutions for secure government and intelligence missions. Build LLM-powered workflows, agent-based automation, data pipelines, and production ML systems. Integrate AI into operational platforms, conduct model evaluation and testing, optimize workflows, and ensure scalability, reliability, security, and governance. Collaborate with mission users, analysts, software developers, and leadership while providing technical guidance and mentoring.

Develop and deploy AI/ML solutions for classified government and intelligence workflows. Responsibilities include building LLM-powered and agent-based automation, integrating AI into operational systems, developing data pipelines, evaluating models, optimizing workflows, and conducting operational testing. The role requires collaboration with mission users, analysts, developers, and leadership while ensuring scalable, reliable, and secure production solutions.

Design and implement AI/ML capabilities for cyber and intelligence workflows, including LLM-powered solutions, agent-based automation, and API or enterprise data integrations. Translate mission-user workflows into automation requirements and executable logic, collaborate with analysts and developers, and ensure reliable production deployment in classified environments. The role requires Python, TS/SCI with polygraph clearance, and qualifying education and experience.

Designs and implements AI/ML capabilities for cyber and intelligence workflows, including LLM-powered systems, agentic automation, API and data-store integrations, and production deployments in classified environments. Works with mission users to document workflows, translate decision points into executable logic, and improve analytical efficiency. Collaborates with operators, analysts, developers, and leadership while ensuring secure, governed, reliable system integration.

Build and lead the organization-wide adoption of generative AI, agentic systems, and AI-assisted development. Design production automation agents, evaluate coding tools, modernize rules-based workflows with ML, and establish responsible AI guardrails. Partner with engineering, product, data, and clinical teams to identify high-impact opportunities, own the enablement roadmap, measure outcomes, and drive adoption through working software, documentation, and hands-on collaboration.

Build, secure, and operate an Azure Government AI platform supporting Azure AI Foundry and approved generative AI models. Configure AI projects, deployments, networking, identities, monitoring, cost controls, and integrations. Automate repeatable infrastructure and release processes, troubleshoot across cloud layers, and create operational documentation. The role requires strong Azure engineering, AI platform, security, networking, monitoring, and scripting experience, with responsibility for reliable developer experiences and governed AI usage.

Lead the design, development, and deployment of scalable machine learning and generative AI solutions. Partner with stakeholders, data scientists, and software engineers to build AI platforms, tooling, agents, and autonomous workflows. Analyze large datasets, optimize and fine-tune models, apply emerging AI technologies, and establish reusable capabilities and technical standards across the organization.

Develop, deploy, and optimize scalable machine learning solutions, including predictive models, deep learning systems, data pipelines, and model-serving infrastructure. Monitor model performance, manage retraining and lifecycle processes, ensure quality and compliance, and support responsible AI practices. Collaborate with data scientists, engineers, product teams, and business stakeholders while researching emerging AI technologies and improving AI architectures.