Top AI Engineer Jobs

Leads the design, operation, optimization, and governance of cloud, data, and AI platforms. Responsibilities include performance monitoring, incident response, disaster recovery, infrastructure design, automation, CI/CD, Azure DevOps workflows, and Azure Machine Learning and AI Foundry deployment pipelines. The role also establishes operational standards, improves observability, manages complex troubleshooting, supports security controls, documents platform processes, and mentors junior engineers.

Build and scale developer platform capabilities, AI-powered coding and review tools, CI/CD systems, Kubernetes infrastructure, and self-service developer workflows. Improve reliability, security, observability, and delivery speed through automation, reusable templates, guardrails, documentation, and incident-response practices. Collaborate closely with platform teammates, contribute to roadmap decisions, and communicate clearly about progress, blockers, and technical tradeoffs.

Staff Software Engineer responsible for architecting reliable internal platforms, integrating legacy and SaaS systems, and applying production-grade generative AI and agentic systems. The role sets technical direction across teams, leads build-versus-buy decisions, improves reliability and security, designs analytics-ready data models, evaluates AI opportunities, manages AI costs and ROI, and mentors engineers while advising leadership.

Build and operate Node.js and TypeScript backend services using durable workflows and AI-assisted development. Define service patterns, orchestrate migrations, maintain MCP and AI workflow tooling, and improve observability through metrics, alerts, dashboards, and tracing. Handle production incidents, contribute targeted frontend changes in React and Angular, and collaborate on architecture and code reviews in a fully remote environment.

Architect and build reliable, scalable AI platform services supporting model training and inference across hybrid-cloud environments. Develop reusable APIs, SDKs, tooling, and reference architectures; establish observability, resilience, security, automation, and cost-optimization standards. Partner with engineering and product teams to deliver platform capabilities, meet reliability targets, resolve production incidents, and drive remediation. Mentor engineers and promote responsible, secure use of AI-assisted development tools.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

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 software engineers and applied AI/ML practitioners while developing secure, scalable production systems in Java or Python. Integrate LLMs, generative AI, NLP, RAG, and coding assistants into software engineering workflows. Establish responsible AI governance, validation standards, testing, security, and operational practices. Guide system architecture, experiments, algorithm implementation, productionization, code quality, automation adoption, and technical decision-making across global teams.

Leads the design, development, and delivery of secure, scalable software and production AI applications using Python, ADK, and LangGraph. Owns system architecture, API standards, microservices, quality controls, guardrails, testing, resiliency, and operational readiness. Partners with Product, Security, SRE, and engineering teams while driving CI/CD, security scanning, dependency management, and SDLC improvements. Provides technical leadership through design reviews, stakeholder alignment, code reviews, and mentoring.

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.

Leads hands-on development of agentic AI solutions on NEO, including agent execution, sandboxing, integrations, memory, retrieval, evaluations, guardrails, and authorization. Builds full-stack TypeScript and Next.js applications, writes secure production code, improves engineering automation, and supports operational stability. Mentors engineers, promotes responsible AI-assisted development, establishes validation standards, and drives adoption of emerging technologies across Software Engineering.

Lead development of secure, scalable Java full-stack applications for mortgage technology. Design, develop, test, troubleshoot, review, and debug production code; improve operational stability through automation and remediation. Drive responsible adoption of enterprise-approved AI-assisted development tools, establish validation and security standards, coach engineers, and evaluate vendor and architectural solutions. Collaborate within agile teams using SDLC, CI/CD, resiliency, and security practices.

Leads full-stack software engineering within an agile Asset Management team. Develops, reviews, tests, debugs, and operationalizes secure production code using Java, Python, AWS, Spring JPA, Hibernate, PostgreSQL, REST, and GraphQL. Drives adoption and governance of AI-assisted engineering practices, validates AI outputs, mentors technical teams, influences architecture and product decisions, and promotes SDLC automation, reusable frameworks, scalability, resiliency, and security.

Leads infrastructure engineering for software-defined retail branch platforms, modernizing C#/.NET and PowerShell systems into AI-native, agentic platforms. Designs agentic workflows, MCP integrations, cloud infrastructure, and automation; resolves complex systems issues; delivers modernization programs; promotes AI-assisted engineering practices; and ensures resiliency, security, compliance, traceability, and auditability.

Leads a team of software engineers in designing, developing, testing, and operating secure, resilient Java microservices. Oversees architecture, implementation, technical troubleshooting, and design documentation for event-driven systems using Kafka, Spring Boot, and cloud infrastructure. Promotes automation, responsible AI-assisted development, secure coding, continuous delivery, operational stability, and adoption of emerging technologies. Provides technical leadership across agile teams and evaluates architectural solutions with vendors and internal stakeholders.

Lead design and delivery of secure, scalable distributed systems and AI-enabled applications. Drive architecture, CI/CD, IaC, and operational excellence; mentor engineers; implement retrieval-augmented generation and AI-assisted engineering practices with appropriate guardrails and validation.

Leads development of secure, scalable Java and AWS software systems, including microservices, event-driven platforms, and production machine-learning systems. Designs data and feature pipelines, model deployment and monitoring, automation, CI/CD, and operational stability practices. Guides code reviews, testing, secure coding, vendor evaluations, and responsible adoption of AI-assisted engineering tools. Collaborates across product, data, platform, SRE, and governance teams while promoting MLOps, LLM, observability, and model-monitoring practices.

Develops secure, scalable full-stack applications for Asset Management. Responsibilities include software design, coding, testing, troubleshooting, architecture, data analysis, visualization, and operational stability. Uses enterprise-authorized AI coding tools responsibly to improve development productivity while validating outputs for security and correctness. Builds services with Hibernate, Spring JPA, PostgreSQL, REST APIs, and GraphQL, with preferred experience in Kafka, Avro, React, and AWS.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms on AWS. Design retrieval/RAG, agents, guardrails, tooling, observability, and APIs. Optimize performance, cost, and security; establish evaluation and testing frameworks; mentor engineers and drive cross-team technical standards in a regulated enterprise.

Build and lead production Python software for technology-risk oversight, including data pipelines, PostgreSQL systems, authenticated APIs, and LLM agent workflows. Develop RAG, tool orchestration, evaluation, and guardrails; ensure security, auditability, observability, testing, reliability, and responsible AI use. Collaborate with Product, Risk, Controls, Compliance, and data teams while delivering dashboards and evidence packages.

Leads full-stack development of secure Private Banking applications and production-grade agentic AI solutions. Builds systems using Python, Java, LLMs, orchestration frameworks, APIs, cloud services, and event-driven architectures. Drives AI-assisted engineering governance, responsible AI practices, security, observability, automation, and production readiness. Collaborates with product, architecture, cybersecurity, data, and business teams; mentors engineers and influences technical direction across globally distributed agile teams.

Lead a global agile scrum team to design, build, and operate scalable, secure microservice-based applications. Integrate AI (Claude Code) into development and delivery, drive architecture and technical decisions, mentor engineers, enforce AI-assisted engineering governance, and ensure production quality, stability, and automation across the SDLC.

Design, build, and operate AI-enabled applications and ML/LLM platforms. Deliver production-quality code, SDK/REST integrations, MLOps pipelines, model/versioning and CI/CD, distributed fine-tuning and evaluation, and reusable platform components. Drive adoption of agentic AI workflows, enforce responsible AI and security controls, automate reliability, and influence senior stakeholders to align platform roadmaps with business outcomes.

Lead design, develop, and deploy secure, scalable full-stack applications (Java or Python) for finance planning and analysis. Drive architecture reviews, operational stability, CI/CD automation, cloud-native AWS deployments, and integration of AI/ML/LLM capabilities. Mentor teams and lead engineering communities of practice.

Lead design, develop, and maintain secure, scalable data and AI-driven systems. Build data pipelines on AWS/Databricks, write production code, drive AI-assisted engineering adoption, enforce validation/security standards, and mentor teams on responsible AI practices.