Top AI Engineer Jobs

Lead design, build, deploy and optimise enterprise AI/ML and generative AI solutions (LLMs, RAG, agents) for production in a regulated financial environment. Drive MLOps, monitoring, governance, and platform integration across cloud-native services while collaborating with product, data, security, and business stakeholders to deliver measurable outcomes and scale AI capability.

Designs secure, scalable cloud-enabled architectures for AI-assisted modernization of legacy Marine Corps systems. Advises customers and technical teams, recommends AI/ML, data, cloud, and DevSecOps patterns, manages technical risks, and builds hybrid deterministic-agentic pipelines using LangChain and LlamaIndex. The role also fine-tunes large language models, integrates code-as-data techniques such as ASTs and control/data flow graphs, and develops production AI systems using Java, Python, APIs, and Databricks.

Leads teams designing and deploying AI-driven enterprise and cloud security solutions. Responsibilities include developing machine learning systems, integrating data infrastructure and pipelines, performing advanced modeling and analysis, and building AI applications with Python, Java, and C++. The role involves client engagement, strategic problem-solving, stakeholder validation, coaching, process innovation, and operational excellence while addressing complex cybersecurity and privacy challenges.

Leads the design and development of AI-driven enterprise and cloud security solutions. Manages teams delivering data, analytics, and machine learning engineering projects; oversees AI model implementation, data infrastructure, pipelines, and platform deployments. Uses Python and C++ for algorithm development, analyzes data, improves data quality, ensures technical compliance, mentors staff, manages client expectations, and drives innovation across cybersecurity and privacy initiatives.

Develop and implement AI and machine learning solutions for Corporate and Investment Banking Operations and Controls. Responsibilities include identifying and prioritizing use cases, building data products and models, collaborating with IT and business stakeholders, applying AI governance and risk controls, testing and monitoring solutions, creating documentation and training materials, and tracking KPIs, ROI, accuracy, and performance. The role also promotes AI best practices and adoption across the organization.

The Applied AI Engineer will build and maintain AI agents, manage integrations and monitor performance, ensuring quality and continuous improvement of AI tools at Zello.

The role involves designing, developing, and optimizing AI software components, collaborating with cross-functional teams, and contributing to the technical vision of AI systems at Capital One.

Lead design, build, deploy, and support scalable, responsible AI systems (foundation model training, LLM inference, similarity search, guardrails, observability). Collaborate with cross-functional teams, optimize model performance and cost, and shape technical vision and roadmap for foundational AI platform services.

Build and ship AI-native workflows and prototypes for finance/accounting use cases, process large unstructured datasets, iterate with customer feedback, and design analytics-driven evaluation systems to improve product stability and value.

Leads the design, development, and deployment of AI-powered healthcare solutions using Java, Spring Boot microservices, HL7 FHIR, Da Vinci guides, Kafka, relational databases, Docker, Kubernetes, and cloud platforms. Builds REST APIs and event-driven systems across claims, eligibility, prior authorization, and provider domains. Establishes CI/CD automation, observability, security, and responsible AI practices while evaluating emerging technologies and improving operational workflows.

Integrate and fine-tune existing AI models within production software, evaluate model performance, build evaluation frameworks, configure observability, develop Python AI features, and troubleshoot real-world performance. Collaborate with data science, engineering, the CTO, and cross-functional teams to improve products and identify AI innovation opportunities. This role focuses on applying existing models rather than designing new machine learning models.

Design and ship production LLM features for a SaaS product: build customer intelligence from recordings and text, prioritize testimonials using metadata, create AI-powered surveys/interviews, iterate via research, prototyping, deployment, and evaluation. Own technical decisions and deliver measurable outcomes working closely with engineering leadership and customers.

Develop and maintain production AI and software solutions for alternative asset management workflows. Embed with business teams to identify use cases, build self-service AI capabilities, and deliver agentic workflows, retrieval pipelines, and evaluation tools. Use Python, SQL, Git, CI/CD, Docker, Terraform, and cloud deployment practices. Transition proofs of concept into production, lead concurrent projects, train technical and business users, and collaborate with stakeholders to define requirements and scope.

Develop and maintain production AI and analytics solutions using Python and SQL. Partner with financial business units to identify workflow improvements, build agentic and retrieval-based AI applications, and transition proofs of concept into reliable deployments. Use Git, CI/CD, Docker, Terraform, and cloud technologies while establishing evaluation tools, prompt libraries, and runbooks. Lead multiple projects, train technical and business users, and communicate requirements, scope, documentation, and changes to stakeholders.

Lead technical projects and design, build, and deploy production-quality AI/ML systems, LLM-based agents, and data pipelines. Explore data, develop AI solutions, shape client proposals, establish evaluation and observability frameworks, optimize model cost and latency, and guide best practices in modeling, code hygiene, and data engineering. Engage directly with clients and lead proprietary AI/ML algorithms, tools, and infrastructure across industries.

Build and operate customer-facing AI agent experiences for loyalty programs, including LLM workflows, tool calling, guardrails, evaluations, monitoring, personalization, and streaming chat. Integrate frontend, backend, APIs, data services, and AI orchestration layers. Partner cross-functionally to ship measurable products, maintain quality, protect sensitive data, and support features in production.

Develops AI-enabled software and rapid prototypes that improve defensive cyber operations for government and Defense Industrial Base clients. Responsibilities include collaborating with mission operators, applying LLMs to accelerate development and analytics, and supporting vulnerability patching, incident response, threat emulation, and COMSEC monitoring. The role requires Python development and cybersecurity knowledge, with preferred experience in AI production systems, data engineering, Splunk, Kubernetes, Rancher, and Apache Airflow.

Build and prototype software that improves defensive cybersecurity operations for government and Defense Industrial Base clients. Collaborate with mission operators to solve workflow challenges, use Python and LLMs to accelerate development and analytics, and support vulnerability patching, incident response, threat emulation, and COMSEC monitoring. Preferred experience includes AI solutions, data engineering, Splunk, Kubernetes, Rancher, and Apache Airflow.

Embed with strategic enterprise customers to prototype, build, and productionize full-stack generative AI and agentic applications; advise on AI strategy, codify repeatable solution patterns, mentor teams, and channel field feedback to product. Focus on scalable, secure AI architectures and LLM/MLOps practices to operationalize RAG, fine-tuning, semantic search, and agentic workflows.

Lead principal for CFD and physics-based simulation focusing on CFD, fire/explosion, refrigerant leakage, battery thermal runaway, combustion, multiphase flows, and electronics cooling. Own V&V strategy and model credibility, enforce ASME/NASA/Oberkampf & Roy best practices, drive test-to-simulation correlation, mentor engineers, produce technical reports for certification, and guide global teams on validated modeling approaches for safety-critical applications.

Use CFD tools (FLACS, FDS, others) to model battery fires, gas dispersion, deflagration, and thermal impacts. Perform pre/post-processing, V&V against large-scale tests, support performance-based design and code compliance, produce technical reports, and collaborate with multidisciplinary teams while developing domain expertise.

Build and validate computational models of combustion, heat transfer, fluid flows, and fire/explosion scenarios using commercial CFD and FEA tools. Perform pre/post-processing, analyze results, document modeling approaches, run validation experiments, write technical reports, and communicate findings to internal and external stakeholders. Collaborate with industry and academics and present at conferences.

The role involves leading AI product development, working with cross-functional teams to enhance AI systems, and optimizing large-scale production AI systems.

Lead design, develop, deploy, and support large-scale AI systems (foundation model training, LLM inference, vector search, guardrails, observability). Partner cross-functionally to optimize performance, scalability, and cost, and shape long-term foundational AI technical vision and roadmap.

Lead technical projects and design, build, and deploy production-quality AI/ML systems, pipelines, and LLM-based agent platforms. Explore data, develop AI solutions, advise partners on proposals, select appropriate modeling approaches, establish evaluation and observability frameworks, optimize system performance and cost, and guide proprietary AI/ML infrastructure development. Engage directly with clients across industries while promoting best practices, responsible AI governance, and robust engineering standards.