Top AI Engineer Jobs

Develop and maintain Python and Golang automation for security and incident response, build SOAR playbooks (Cortex XSOAR), integrate systems via RESTful APIs, support AI initiatives (RAG, MCP, Agent communications), participate in Agile sprints, conduct code reviews and testing, and produce technical documentation.

Lead design, development, testing, and deployment of cloud-enabled AI/ML systems (computer vision, sensor fusion, decision support) for maneuver, surveillance, and engagement missions. Evaluate model performance on operational datasets, optimize for robustness and cyber resilience, troubleshoot deployed systems, and produce technical documentation and reports for stakeholders.

Design, develop, test, and deploy fullstack software solutions. Collaborate with product, operations, AI engineers, and data scientists. Implement CI/CD, contribute to architecture, and maintain cloud-deployed services.

Build infrastructure and AI systems for Hardware Intelligence: agent infrastructure, retrieval and evaluation pipelines, and tooling to reason over large-scale time series, telemetry, and engineering datasets. Prototype and ship AI-powered workflows for anomaly investigation and root cause analysis, work directly with customers, and establish engineering patterns as the platform scales.

Design, build, and maintain scalable MLOps pipelines and data workflows to train, fine-tune, deploy, and monitor multilingual text and speech translation models. Prepare and optimize multilingual datasets, support model evaluation and benchmarking, and enable low-resource language training while collaborating with data scientists to integrate models into production.

Lead technical relationships with major AI labs to define joint roadmaps, design and build platform-to-platform integrations, create POCs and reference architectures, enable strategic customers, and produce field enablement content. Act as hands-on technical owner across product, engineering, partnerships, and GTM to drive joint AI solutions.

Own the full product loop: identify high-impact problems, rapidly prototype and ship AI-accelerated solutions, build internal tools and automations, collaborate with operations and business teams, and iterate based on usage and data to drive adoption and business impact.

Design and maintain scalable backend systems for multi-channel, AI-driven messaging. Collaborate cross-functionally to build high-performance services, optimize message delivery at massive scale, improve code quality, reduce technical debt, and drive architectural decisions using advanced AI/LLM technologies.

Design, develop, test, document, and deploy state-of-the-art AI/ML applications (LLMs, CV, sensor/data fusion). Collaborate cross-functionally to productionize models, define software/testing requirements, perform rapid prototyping, and follow software engineering best practices. Must hold active US Government Secret clearance.

Design, build, and operate cloud-native data platforms and AI-powered applications. Implement ETL/ELT pipelines, transform structured and unstructured data, optimize data architectures for performance and scale, and collaborate with cross-functional teams to turn strategy into production solutions. Support operations, ensure data quality, and stay current with emerging AI and data technologies.

Design, prototype, and deploy AI-powered solutions by partnering with business stakeholders and engineering teams. Analyze processes, identify AI automation opportunities, build POCs/MVPs using LLMs and agents, define KPIs, document and train users, and drive adoption and scalability of reference AI solutions across the organization.

Design and prototype AI-driven solutions with business stakeholders, translate requirements between business and engineering, measure ROI and KPIs, document and train users, drive adoption of LLMs and AI agents, and mentor future AI practitioners to scale Citi's AI capabilities.

Partner with business and technology leaders to analyze processes, design and prototype AI/LLM-driven agent solutions (POCs/MVPs), measure KPIs, document and train users, drive adoption and scaling of reference solutions, and mentor future AI engineers to sustain enterprise AI capabilities.

This role involves designing, developing, and maintaining AI/ML products, collaborating with stakeholders, and ensuring model deployment on AWS. Responsibilities include creating APIs, establishing MLOps practices, and driving AI strategy for operational improvements.

Build and operate production-grade AI agent systems and connectors into governed data (Bronze layer). Design multi-agent workflows, enforce data contracts, implement observability, guardrails, cost controls, governance, and durable documentation while collaborating with stakeholders.

Design and build the LLM orchestration layer and multi-agent workflows that enable streaming chat, tool use, memory, function-calling, and autonomous reasoning; prototype agent behaviors; integrate LLMs with frontend/backend systems; evaluate frameworks for performance, safety, and extensibility.

Design, build, and test software for spacecraft and ground systems across the full stack. Develop flight and real-time control software, simulation and hardware-in-the-loop testing frameworks, integrate with hardware teams, support system bring-up and on-orbit operations, and create tooling for automation and mission operations in resource-constrained, high-reliability environments.

The Innovations and AI Solutions Engineer develops and optimizes AI solutions, manages data flows, implements machine learning models, and collaborates with users on data analysis and reporting.

As a .NET Developer (AI), you will design enterprise applications with AI capabilities, optimize agent behavior, and collaborate across teams to drive innovation in solutions.

As a Research Engineer, you will tackle AI safety challenges, develop evaluation methodologies, conduct adversarial testing, and work on model alignment and interpretability. You'll bridge theoretical and practical applications of AI safety research and collaborate with product teams to implement safety solutions.

Design, build, deploy, and maintain AI-integrated full-stack web applications, chatbots, and dashboards. Collaborate with ML and backend teams to ensure secure, scalable, high-availability production systems and monitor model outputs and training analytics.

The role involves automating software testing processes for vehicle systems, developing AI-assisted workflows, improving testing efficiency, and collaborating with engineering teams.

Manage model launches and provider onboarding in an AI marketplace, focusing on integration, QA, and internal tooling improvements.

As a Senior Full-Stack Software Engineer, you will design, build, and ship AI-powered systems to optimize workflows in the single family rental market, focusing on backend services and full-stack development in a fast-paced environment.

Design and implement .NET/C# features integrating AI services (agentic workflows, RAG, vector DBs). Improve performance, contribute to architecture, use source control, participate in code reviews, and collaborate cross-functionally to deploy scalable AI-enabled solutions.