Top AI Engineer Jobs

Leads full-stack software engineering teams, technology portfolios, and large-scale cloud-native initiatives. Responsibilities include setting technical strategy, managing engineering talent, driving architectural and roadmap decisions, delivering distributed microservices and full-stack systems, and aligning engineering execution with enterprise goals. The role involves AWS and other cloud technologies, modern programming languages, databases, containers, Agile delivery, AI-enabled development, and regulatory technology solutions.

Lead product definition, technical strategy, customer discovery, and go-to-market execution for MCU-based server platform-management solutions. Define silicon, firmware, protocol, SDK, and reference-design requirements for distributed control across compute, accelerator, storage, power, and cooling systems. Engage hyperscalers, OEMs, ODMs, and ecosystem partners; evaluate standards and competitive solutions; create positioning, launch plans, business cases, roadmaps, and sales enablement. Coordinate architecture, engineering, applications, sales, and strategic customer teams through product development, interoperability validation, and customer design-ins.

Develop and deploy AI solutions across the organization, integrate internal applications with AI agents and APIs, evaluate external AI technologies, and build scalable infrastructure. The role also provides access to AI tools, trains colleagues, identifies emerging use cases, and collaborates with software teams on practical AI adoption.

Build infrastructure for AI-driven software development, including cloud coding environments, autonomous coding agents, SRE integrations, and reusable agent platforms. Own developer environments, agent integrations, security guardrails, observability, evaluation, and feedback loops. The role requires deep infrastructure experience across AWS, containers, infrastructure as code, CI/CD, and TypeScript/Node.js, plus hands-on use and orchestration of AI coding agents.

Leads Python and generative AI application architecture, development, and delivery across investment banking and institutional client functions. Evaluates AI, machine learning, robotic process automation, intelligent data processing, and predictive analytics solutions. Manages project scope, budgets, schedules, resources, quality, SLAs, and stakeholders while mentoring technical teams and translating business needs into scalable AI-enabled solutions.

Designs and delivers enterprise web applications using Angular, Java, Spring Boot, microservices, relational and NoSQL databases. Leads system architecture, security, scalability, testing, CI/CD, and production readiness while leveraging AI coding agents. Owns delivery end to end, integrates agentic workflows, manages multiple workstreams, and coaches engineers in AI-first development. Applies Corporate and Investment Banking knowledge to deliver business-focused technology solutions.

Build and scale enterprise generative and agentic AI platforms for software delivery. Develop multi-agent workflows, RAG pipelines, evaluation harnesses, integrations with enterprise tools, and production microservices using Python and Java. Design resilient distributed systems, APIs, retrieval architectures, observability, CI/CD, containerized deployments, and secure guardrails. Collaborate with subject matter experts and frontend teams to deliver production-ready AI applications and forward-deployed solutions.

Leads the design, development, and deployment of scalable full-stack applications for M&A technology using Java, Angular, microservices, and event-driven architectures. Integrates agentic AI, GenAI, and LLM capabilities into regulated enterprise systems. Oversees software lifecycle activities, cloud and DevOps practices, security, testing, operational readiness, and risk. Mentors developers, conducts code reviews, partners with architecture and business stakeholders, and promotes modern engineering standards.

Develop and maintain full-stack applications with front-end and back-end components, AI-assisted coding, Oracle PL/SQL, RESTful APIs, testing, CI/CD deployment, and troubleshooting. Participate across the software development lifecycle, advise stakeholders, assess technology risks, support business users, establish operating standards, and coach junior analysts. Work independently as a subject matter expert within an Agile technology team.

Build, test, deploy, and maintain secure, scalable full-stack enterprise applications and prototypes. Develop front-end experiences, backend services, APIs, integrations, authentication, and data persistence. Use AI coding assistants and integrate generative AI, RAG, agents, and tool calling when appropriate. Operate cloud-native solutions with CI/CD, infrastructure as code, automated testing, observability, and security controls. Collaborate with product managers and architects, troubleshoot production systems, document solutions, and communicate technical risks and tradeoffs.

Develops and industrializes AI-enabled engineering workflows across design, simulation, testing, and manufacturing. Responsibilities include building agentic AI optimization systems, integrating AI with engineering tools, developing multiphysics digital twins, creating physics-informed co-simulation engines, training CAD-aware transformer and graph neural network models, and deploying enterprise-scale digital threads.

Leads the design, development, deployment, and governance of enterprise-grade Python-based generative AI platforms and services. Defines architecture with technical, data science, and business stakeholders; drives production implementations, testing, monitoring, scalability, and risk controls. Manages complex initiatives and vendor partnerships while advising developers and analysts. Requires strong Python, AI/ML, cloud, CI/CD, software engineering, leadership, communication, and project management expertise.

Design and prototype AI-powered solutions that transform business processes. Collaborate with stakeholders and engineering teams, translate business needs into technical requirements, develop AI agents and LLM solutions, measure initiative impact, document processes, train users, and mentor future AI practitioners. The role requires hands-on AI/ML expertise, business acumen, strong communication, and experience deploying solutions in business contexts.

Design, build, and scale enterprise LLM-powered applications and multi-agent systems using ADK and LangChain. Develop AI pipelines, API integrations, guardrails, observability, and production deployment frameworks. Partner with Risk, Compliance, and Model Governance teams to meet financial services regulatory requirements, lead end-to-end delivery, establish engineering standards, and mentor technical teams.

Design, build, and scale enterprise-grade LLM and agent-based systems using ADK, LangChain, RAG, and tool orchestration. Develop guardrails, AI pipelines, API integrations, monitoring, and performance improvements while meeting financial-services risk, compliance, and model-governance requirements. Lead production delivery, establish engineering standards, and mentor teams in partnership with Technology, Product, Risk, and Compliance.

Develop and maintain scalable Python applications for Generative AI, integrating APIs such as OpenAI and Gemini and implementing RAG systems. Build backend services with Python frameworks, optimize application performance and security, write unit and integration tests, participate in code reviews, and collaborate with product, data science, and engineering teams. The role also involves application lifecycle management, user support, risk assessment, mentoring, and advising senior stakeholders.

Develop and maintain scalable Python applications for Generative AI, integrating APIs such as OpenAI and Gemini and implementing RAG systems. Build backend services with FastAPI, Flask, or Django; write tests; optimize performance, scalability, and security; and collaborate with product, data science, and engineering teams. The role also involves application lifecycle oversight, user support, code reviews, risk assessment, mentoring junior analysts, and advising stakeholders.

Designs, develops, deploys, and operates scalable AI and machine learning solutions across the enterprise. Responsibilities include building end-to-end ML infrastructure, managing LLM lifecycles, optimizing RAG systems and prompts, evaluating models, deploying Azure-based solutions, writing tested software, supporting CI/CD and DevSecOps, monitoring production systems, and collaborating with engineering, architecture, DevOps, and testing teams.

Design, develop, and deploy enterprise AI applications and agentic solutions using LLMs, RAG, intelligent automation, and machine learning. Build data pipelines, APIs, microservices, and scalable AI components with Python or Java. Collaborate with architects, product owners, researchers, and engineering teams in a regulated financial environment. Lead multiple AI use cases, oversee AI engineers within a Center of Excellence, document implementations, and evaluate emerging AI technologies.

Develop and maintain Python-based AI applications under senior guidance. Collaborate on requirements and feature implementation, use Pydantic for data validation, write documented code, participate in reviews, troubleshoot issues, and support testing and CI/CD. The role requires Python, object-oriented programming, REST APIs, and knowledge of LLMs, with opportunities to learn modern frameworks, backend development, data engineering, and cloud platforms.

Design and prototype AI-powered solutions that transform business processes. Partner with business and technology leaders to identify automation opportunities, translate business needs into technical requirements, develop AI agents and proof-of-concepts, measure impact, document solutions, train users, and support scalable adoption. The role requires hands-on expertise in LLMs, machine learning, NLP, data modeling, and strong communication with technical and nontechnical stakeholders.

Build and operate integrations and automations using iPaaS and APIs, ship production AI/LLM agents and workflows, implement identity-driven provisioning (SSO/SCIM/OAuth), and deliver monitored, reliable automations with runbooks while partnering across teams to scale systems.

Designs, deploys, operates, and improves secure hybrid and multi-cloud AI platforms. Builds infrastructure automation, CI/CD pipelines, observability, monitoring, logging, and governance practices. Supports Kubernetes and containerized applications, troubleshoots complex systems, coordinates technical projects, evaluates vendor releases, and collaborates with developers, architects, security teams, networks, and stakeholders to deploy reliable AI services.

Designs, builds, deploys, and operates AI-powered software for mortgage operations and customer experiences. Responsibilities include integrating LLMs and predictive models, developing cloud-native applications on Azure, managing Docker and Kubernetes deployments, creating CI/CD pipelines, implementing infrastructure as code, monitoring production services, optimizing AI workloads, and ensuring security, compliance, fair lending, and responsible AI practices. The role also partners with business teams, guides AI engineering standards, and contributes to long-term technology strategy.

The Principal Software Development Engineer leads the architecture and delivery of AI-driven healthcare applications, overseeing full stack development and collaborating with stakeholders to align technical solutions with business needs.