Top AI Engineer Jobs

Build the core AI intelligence layer for an energy infrastructure platform. Own technical strategy, architecture, production engineering, agentic workflows, data and geospatial pipelines, security, and observability. Translate energy procurement and project finance workflows into software, automate contracting and diligence, and iterate directly with users to achieve product-market fit. This founding role requires autonomous execution, rapid prototyping, technical leadership, mentorship, and comfort with startup ambiguity.

Build and improve production generative AI systems that create editable motion from natural language. Responsibilities include model orchestration, structured generation, validation, compiler-backed quality gates, fine-tuning, post-training, synthetic data, evaluation, dataset governance, regression testing, and production reliability. The role requires hands-on ownership across LLM behavior, data pipelines, infrastructure, inference optimization, and product quality, with code generation, compilers, multimodal systems, or animation experience being valuable.

Design, train, test, deploy, and maintain AI/ML models (LLMs, RAG, agentic workflows). Build AI-enabled applications and secure data foundations on cloud (AWS). Implement MLOps/DevSecOps, event-driven integrations, and collaborate with engineers, architects, and product owners to deliver mission-focused Air Force solutions. Secret clearance required.

Leads an Agile Release Train delivering an agentic AI web application across product, engineering, AI/ML, architecture, security, QA, DevOps, and operations teams. Facilitates PI Planning, dependency and risk management, release readiness, production deployment, metrics, and continuous improvement. Coordinates AI experimentation and production delivery while addressing model reliability, safety, latency, cost, integrations, observability, and operational risks. Serves as the execution integrator between technical teams, product leadership, and enterprise stakeholders.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

Lead design and delivery of full‑stack, cloud‑native AI and GenAI solutions; define AI strategies; architect secure, scalable platforms; mentor engineering teams; run pilots, integrate successful technologies, and partner across cloud, security, data, and business stakeholders to improve developer experience and drive enterprise adoption.

Designs, develops, and supports secure AI-powered applications, agents, APIs, integrations, and workflow automations. Builds Generative AI solutions using LLMs, prompt engineering, retrieval patterns, and enterprise data. Responsibilities include full software delivery lifecycle participation, AI output evaluation, stakeholder collaboration, secure engineering, governance, monitoring, risk controls, and operational readiness.

Lead end-to-end development of a greenfield enterprise platform for software dependency risk management. Build Java services, APIs, data ingestion pipelines, dependency analysis capabilities, and developer-facing features on AWS. Guide engineers and teams in secure AI-assisted development, code quality, testing, governance, and operational stability. Provide technical direction, influence architecture and product decisions, support production systems, and collaborate with product managers and designers to deliver scalable platform capabilities.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Lead the research, architecture, and production delivery of agentic and generative AI solutions for complex banking operations. Design collaborating agents, reusable services, evaluation frameworks, and distributed machine learning systems. Establish experimentation, monitoring, safety, reliability, and governance practices while partnering with business, product, and engineering teams. Mentor engineers and researchers and drive technical alignment across stakeholders.

Design, deploy, and improve enterprise AI solutions including LLM workflows, RAG pipelines, agents, classifiers, and copilots. Develop prompt strategies, tool-calling patterns, guardrails, evaluation frameworks, and responsible AI controls. Partner with data, security, privacy, compliance, architecture, and QA teams to ensure solutions are safe and governed. Monitor production quality, accuracy, hallucination risk, latency, cost, observability, and model drift while documenting limitations and operational requirements.

Designs, deploys, maintains, and troubleshoots HPE NonStop payment infrastructure, secure key managers, Etinet servers, and related subsystems. Uses AIOps and AI-assisted practices for incident triage, anomaly detection, automation, documentation, and reliability improvements. Partners with SRE, observability, and cybersecurity teams to govern AI-enabled monitoring, maintain auditability, execute upgrades, and produce operational knowledge such as runbooks and troubleshooting guides.

Lead the design, development, and operation of an enterprise GenAI serving platform. Build high-throughput LLM inference, gateway, routing, GPU optimization, quantization, distributed serving, and observability capabilities. Establish reliability practices, SLOs, performance benchmarks, cost controls, and secure production standards. Drive adoption of agentic AI-enabled engineering workflows with governance, auditability, and human validation while influencing senior stakeholders and enabling reusable platform components across teams.

Lead an agile engineering team to design, build, and operate secure, scalable Java/React cloud-native applications on AWS. Drive adoption of AI-assisted development practices, evaluate vendor solutions, provide technical guidance, and ensure production quality, stability, and secure responsible AI usage across the SDLC.

The Staff AI Engineer will analyze AI quality, implement improvements, manage projects, collaborate with teams, and shape AI capabilities in a remote-first environment.

Design, build, and maintain AI agents, applications, and automations for finance stakeholders. Translate ambiguous business questions into decision-ready answers, shape data models and semantic views (dbt on Snowflake), prototype rapidly with AI coding tools, and partner closely with business users to deliver production-grade solutions.

Leads software engineering and quality engineering for JPMorganChase’s Payments team. Builds and maintains large-scale test automation frameworks, integrates automated testing into CI/CD pipelines, and supports application development, system design, and operational stability. Drives responsible use of AI-assisted engineering tools, improves test quality and delivery speed, and establishes validation, security, and resiliency standards. Oversees complex, high-risk projects and promotes practices such as failure triage, contract testing, service validation, and canary testing.

Build and maintain end-to-end full-stack applications, backend services, modern UIs, AI agents, LLM workflows, automation solutions, third-party integrations, and AWS deployments. Own systems from development through production, troubleshoot complex issues, and contribute independently in a fast-paced R&D environment.

Build foundational systems for AI models across data modalities, develop agentic systems and specialized models, and implement reinforcement learning and active inference solutions. The role involves hands-on architecture and product development, requiring Python backend expertise, cloud deployment knowledge, DevOps practices, and the ability to contribute across the technical stack in an early-stage startup.

Design, build, deploy and operate production-grade generative AI and ML solutions (LLMs, RAG, vector search) on cloud-native architectures. Ensure code/model quality, observability, governance, compliance and performance; collaborate with cross-functional teams, mentor engineers, and contribute reusable frameworks and responsible-AI controls.

Leads development of AWS-native software engineering and automation solutions, including IaC, CI/CD, provisioning, observability, and lifecycle orchestration. Builds secure, resilient agentic AI integrations using LLMs and cloud services, improves operational stability, evaluates architectures and vendors, and promotes AI-assisted engineering practices. Mentors teams, establishes validation and responsible AI standards, and leads software engineering communities of practice.

Own technical relationships with strategic enterprise customers throughout Moveworks implementations. Design AI agent solutions, enterprise integrations, workflows, and automations using APIs, iPaaS tools, cloud functions, and scripting. Advise customers on agentic AI roadmaps, coordinate cross-functional delivery, develop reusable capabilities, and mentor engineers. The role combines solution architecture, hands-on implementation, technical consulting, executive communication, and up to 25% travel.

Design, build, and operate production AI automations and agentic workflows for Toast’s customer care systems. Integrate LLMs with Salesforce Service Cloud, Amazon Connect, and workflow platforms; develop autonomous actions with human oversight; test, monitor, and improve system reliability, accuracy, latency, and cost. Collaborate with software engineers, Salesforce developers, and care operations stakeholders to translate workflows into scalable intelligent solutions.

Build AI applications, agents, integrations, and automated workflows that transform technical information into customer documentation. Partner with the Documentation team to use LLMs, agentic AI, and automation to improve content creation, maintenance, publishing, and delivery. Reduce manual effort, increase access to technical knowledge, and identify additional customer-facing workflows where AI can improve efficiency and experience.

Build and scale AI platform infrastructure for internal agents across AWS/GCP. Integrate identity (Okta, OAuth2/OIDC), implement agentic security, containerized deployments (Docker/Kubernetes), CI/CD and Terraform-based pipelines, run vendor evaluations, and design observability/telemetry for LLM-driven workflows while collaborating with security, identity, and engineering teams.