Top AI Engineer Jobs

Leads AI engineering strategy across clinical, patient, and operational products. Designs and operates production retrieval, orchestration, agentic, and evaluation systems, establishing standards for AI safety, observability, responsible deployment, and reliability. Partners with clinical, product, and executive leaders on the AI roadmap, mentors engineers, evaluates emerging models and tools, and codes regularly in Python.

Build enterprise AI platform capabilities and production AI applications, including agentic workflows, retrieval-augmented generation, semantic search, vector databases, cloud-native services, and AI model operations. Collaborate with senior engineers on orchestration, developer tooling, governance, security, responsible AI, monitoring, testing, and compliance. Use Python, Google Cloud Platform, Kubernetes, CI/CD, and infrastructure as code while developing scalable AI solutions.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Design, build, and deploy scalable AI/ML models and pipelines for consumer healthcare. Develop production code (Python/Java/Scala), automate infrastructure (Terraform, Kubernetes), validate model performance, integrate cloud services (Azure/AWS), and collaborate cross-functionally to monitor and document enterprise-scale AI solutions.

Build and deploy AI-powered products and internal tools by working directly with users to identify high-impact problems. Write production code, automate workflows, improve customer experiences, and own the full product cycle from discovery through deployment and adoption. Collaborate with sales, onboarding, and account management teams to drive measurable business outcomes. The role is highly autonomous, in-person, and involves regular client travel.

Build and operate Citi’s scalable Generative AI common platform across on-premises and cloud environments. Responsibilities include designing reliable software, applying LLMs and RAG, partnering with development teams and business experts, maintaining security and SDLC compliance, and evolving the platform at scale. The role requires strong Python, distributed systems, architecture, Kubernetes, cloud, CI/CD, and MLOps expertise, along with leadership, project management, and communication skills.

Develop and maintain AI-powered applications, agents, automations, and integrations for legal and operational workflows. Build LLM tools, RAG systems, APIs, and workflow automations using Python and/or JavaScript/TypeScript. Manage projects from prototype through deployment, evaluate AI performance, maintain secure integrations, and collaborate with attorneys, leadership, and technical teams to improve internal processes.

Design, deploy, and operationalize enterprise AI systems integrating LLMs with legacy architectures. Responsibilities include architecting scalable infrastructure, configuring distributed compute clusters, developing low-latency data pipelines, optimizing networks, implementing secure APIs and data governance, establishing MLOps practices, and mentoring engineers. The role supports mission-driven government and commercial client solutions and requires collaboration with data scientists, systems engineers, and stakeholders.

Design and implement AI architectures and solutions for client business needs. Develop innovative solutions using generative and agentic AI, deep learning, computer vision, NLP, and related technologies. Apply software engineering expertise, work independently, mentor team members, and potentially lead Agile teams. The role requires a bachelor’s degree, Secret clearance eligibility, and at least two years of relevant experience.

Build and deploy production AI/ML solutions for manufacturing, including predictive maintenance, defect detection, anomaly detection, process optimization, quality prediction, and engineering productivity. Own the full AI lifecycle from data preparation and experimentation through deployment, monitoring, and improvement. Collaborate with manufacturing, quality, equipment, automation, and software teams in an active manufacturing environment.

Design, develop, deploy, and maintain machine learning and AI solutions for clinical care and business operations. Coordinate the machine learning lifecycle, implement CI/CD and model-training practices, analyze large datasets, create visualizations and leadership reports, document models, and translate technical findings for business stakeholders. Lead cross-functional projects involving clinical leaders, data engineers, developers, and administrative teams.

Explores and prototypes emerging AI capabilities, including LLMs, agent frameworks, MCP, and retrieval-augmented generation, using real enterprise workflows and data. Evaluates technologies, builds sandbox demonstrations, presents findings to technical and executive audiences, engages vendors, documents experiments, and hands validated prototypes to delivery teams. The role requires strong software engineering, rapid prototyping, independent judgment, business translation, and preferably upstream oil and gas experience.

Supports the design, development, testing, deployment, and optimization of AI, machine learning, generative AI, data engineering, and analytics solutions. The role may focus on AI engineering or data engineering and analytics, including intelligent agents, RAG applications, APIs, data pipelines, exploratory analysis, and data preparation. Associates collaborate with technical teams and stakeholders while applying cloud technologies, security, governance, privacy, and responsible AI practices.

Leads end-to-end delivery of production AI, generative AI, and agentic AI solutions. Designs architectures using multi-agent orchestration, RAG, hybrid retrieval, and human-in-the-loop workflows; establishes evaluation, testing, observability, safety, and responsible AI practices. Partners with business and technical teams to measure value, mentors engineers and data scientists, defines engineering standards, and influences enterprise AI strategy and roadmaps.

Design and deploy production-grade generative AI applications, LLM solutions, and intelligent agents that automate complex enterprise workflows. Build scalable Python systems, RAG data layers, and containerized deployments in OpenShift. Apply rigorous engineering practices, including testing, code reviews, documentation, and CI/CD. Research emerging AI techniques, mentor colleagues, deliver iteratively in an agile environment, and implement responsible AI guardrails to mitigate operational and regulatory risks.

Design, develop, deploy, and support production-grade AI agents and applications. Build agent architectures with orchestration, tool use, state management, evaluation, observability, and secure operations. Integrate enterprise and geospatial data with large language models using RAG and MCP. Develop Python, SQL, R, RESTful services, Node.js applications, dashboards, and Oracle Database integrations while collaborating with technical and mission stakeholders.

The Staff Software Engineer will lead the development of cloud-native AI platforms, optimizing AI workload deployment in Kubernetes environments and collaborating with cross-functional teams.

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

Design, build, and deploy scalable ML pipelines for multimodal neuroscience data. Research, train, and deploy advanced deep learning and reinforcement learning models to predict brain states, detect pathological patterns, and generate personalized neuromodulation parameters. Select AI frameworks and cloud infrastructure, collaborate with computational neuroscientists, and contribute to IP and publications.

Build and maintain scalable AI Search software across the full development lifecycle, including Java and front-end development, ServiceNow administrative tools, search UI components, GraphQL APIs, analytics dashboards, and machine-learning functionality. Own design, coding, testing, deployment, reliability, and performance optimization. Develop automated and manual tests using Jenkins, JUnit, Playwright, Selenium, and related frameworks. Collaborate with Product Owners and engineering teams in Agile Scrum processes while supporting customer-reported search UI and UX issues.

Build and deploy AI agents, analytical tools, and automated workflows for strategy, deal sourcing, market intelligence, CRM operations, due diligence, and executive decision-making. Collaborate with the CSO and cross-functional teams while contributing to Walden’s shared agent platform. Own agent feedback loops, evaluations, observability, optimization, and reusable infrastructure using modern LLM tools, Python, data engineering, APIs, and workflow automation.

Lead optimization strategy and technical development for AI inference workloads on Intel GPUs. Architect high-performance software across compilers, runtimes, frameworks, GPU kernels, and hardware interfaces. Profile and benchmark complex workloads, identify bottlenecks, influence hardware-software co-design, evaluate emerging AI technologies, drive cross-functional initiatives, and mentor engineers. The role focuses on LLMs, generative AI, neural network execution, scalability, and performance optimization.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

Develop AI communication systems for humanoid robots, including generative AI, NLP, speech-to-text, text-to-speech, computer vision, multimodal interaction, gesture recognition, and emotion-aware capabilities. Optimize transformer-based models for real-time edge execution and integrate speech, vision, and movement for natural human-robot interactions.

Build production AI agents and agentic workflows for clinical trial platforms using the .NET stack. Integrate LLM APIs, implement tool use, orchestration, RAG pipelines, vector stores, evaluation, observability, and regulated-environment risk controls. Deliver AI features end to end from backend services through usable front-end experiences, while using Claude Code or equivalent agentic tooling daily.