Top AI Engineer Jobs

Leads the AI-first engineering vision and roadmap for an enterprise platform. Drives AI-enabled SDLC adoption, generative and agentic AI implementation, intelligent automation, governance, responsible AI controls, model evaluation, security guardrails, and DevSecOps automation. Mentors engineering teams on prompt engineering, RAG, agent frameworks, observability, and AI architecture while serving as a senior technical escalation point for cloud, data, application, and production issues.

Design and develop scalable software systems and production LLM solutions using RAG, agentic workflows, PEFT, and guardrails. Own architecture, technical execution, SDLC activities, cloud and Databricks governance, DevOps automation, testing, deployment, and system reliability. Collaborate with cross-functional teams, conduct code reviews, translate business requirements, and guide software design. The role is client-facing and follows a hybrid work model with travel as needed.

Leads the architecture, development, and operation of a scalable AI/ML platform supporting deep learning, LLMs, agentic AI, MLOps, and LLMOps. Optimizes compute and storage resources, builds reusable APIs and frameworks, manages data pipelines, and establishes deployment, monitoring, evaluation, governance, security, and responsible AI standards. Provides technical leadership, mentors engineering and data science teams, and drives cross-functional platform improvements.

Build and operate production-grade AI developer tooling, agentic workflows, and shared infrastructure that improve software development across engineering teams. Responsibilities include AI-assisted code review, testing, issue triage, model integrations, evaluations, monitoring, cost controls, adoption enablement, and cross-functional collaboration with security, platform, quality, product, and design teams.

Build and deploy AI-native products that transform large-scale geospatial data into actionable intelligence. Design multi-agent workflows, integrate frontier LLMs, engineer tool-using agents, and develop data pipelines for anomaly detection and predictive analytics. Ensure secure, scalable cloud integrations and operate production AI systems using DevOps, containerization, orchestration, and CI/CD. Establish feedback and reinforcement mechanisms to improve model reliability while contributing to AI-first engineering standards and documentation.

Develop and deploy foundational AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize large-scale AI systems for cost, latency, throughput, scalability, and reliability. Own complex AI architecture, define reliability objectives, improve GPU/TPU utilization, lead technical reviews, ensure security and compliance, and mentor engineering colleagues.

Design, develop, deploy, and support enterprise AI systems, including foundation models, LLM inference, agentic workflows, vector search, model evaluation, governance, and observability. Establish AI architecture standards, optimize model performance, cost, latency, and scalability, and lead model routing and orchestration initiatives. Drive responsible AI practices, contribute to technical roadmaps, mentor engineering teams, and influence cross-functional stakeholders and enterprise AI strategy.

Build and own production agent systems, including orchestration loops, tool use, retrieval-augmented generation, evaluation gates, and MCP servers. Design reliable control flow with retries, timeouts, state management, grounding, and failure recovery. Develop regression corpora and judge-based gates to determine whether agent-generated code and content can ship safely. Maintain APIs under malformed or hostile inputs and evolving system rules while delivering both architecture and deployed code.

Design, develop, deploy, and operate production-grade AI solutions for forecasting, planning, and scheduling. Build LLM applications, RAG pipelines, and controlled AI agents with grounded recommendations, human oversight, monitoring, evaluation, and responsible AI controls. Develop Python services and APIs using testing, CI/CD, and containerization. Partner with engineering, product, data teams, and end users to translate workflows into secure, scalable, trustworthy AI solutions.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Leads engineering for enterprise agentic AI and LLM platforms. Designs and deploys production-grade LLM services, RAG solutions, autonomous agents, APIs, and cloud-native AWS architectures. Provides technical leadership, architecture guidance, code reviews, MLOps, monitoring, governance, and operational excellence. Drives responsible AI practices and adoption of AI-assisted development across teams, emphasizing security, auditability, human oversight, scalability, and performance.

Design, develop, deploy, and optimize production AI systems, including foundation models, LLM inference, agentic workflows, vector search, guardrails, and model evaluation. Lead architecture, cost-performance governance, technical reviews, and long-term roadmap development. Apply advanced optimization techniques to improve scalability, latency, throughput, hardware utilization, and inference costs while ensuring responsible AI standards. Mentor senior technical leaders and collaborate with engineers, scientists, technical program managers, and product managers.

Build and ship production AI systems across the machine learning lifecycle, including model development, evaluation, serving, monitoring, and API development. Contribute to generative AI, document classification, extraction, LLM, and internal platform tooling. Collaborate with engineers, data scientists, and product stakeholders while developing software engineering, cloud infrastructure, CI/CD, and DevOps skills under mentorship.

Design and deliver enterprise-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Set architecture and optimization strategies for scalable, cost-efficient, responsible AI platforms. Lead model routing, caching, orchestration, and cloud deployments while mentoring engineers, influencing stakeholders, and advancing AI research into production.

Build and deploy scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model training and inference for cost, latency, throughput, and hardware utilization. Own complex AI architecture, reliability SLOs, and technical roadmaps while collaborating across engineering, research, infrastructure, and product teams. Lead deployment reviews for security and compliance and mentor senior technical staff.

Develop and deploy scalable AI systems, including foundation models, LLM inference, agent workflows, vector search, guardrails, evaluation, and governance. Optimize model performance for cost, latency, throughput, and accuracy; build training and fine-tuning pipelines; collaborate with research and data engineering teams; and mentor junior engineers while advancing responsible AI practices.

Designs, develops, deploys, and optimizes enterprise AI systems, including foundation models, LLM inference, agentic workflows, similarity search, model routing, and observability. Establishes AI architecture standards, improves system cost and performance, champions responsible AI, and leads technical direction, mentorship, and cross-functional collaboration across Capital One.

Design, develop, optimize, deploy, and support large-scale AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead optimization for cost, latency, throughput, and hardware utilization; establish AI engineering standards; guide technical roadmaps and design reviews; and mentor senior AI engineers.

Design, develop, deploy, and support scalable AI systems, including foundation model training, LLM inference, agentic workflows, similarity search, guardrails, evaluation, governance, and observability. Optimize model performance, cost, latency, throughput, and hardware utilization. Define AI architecture, reliability objectives, and technical roadmaps while ensuring security, compliance, and ethical alignment. Lead technical reviews and mentor engineering teams across complex AI product initiatives.

Build and deploy production-grade full-stack AI applications for semiconductor manufacturing and autonomous operations. Responsibilities include data pipelines, model training and inference, APIs, web interfaces, digital twin integrations, robotics and sensor integrations, LLM and agentic AI capabilities, and cloud or edge deployments. The role also covers containerization, Kubernetes, CI/CD, GPU optimization, simulation, monitoring, and responsible AI practices.

Build and operate AI-enabled capabilities for Ford Credit’s loan origination platform. Responsibilities include developing audited MCP tools, multi-agent workflows, hybrid graph and vector retrieval, deterministic Java/Kotlin backend services, evaluation systems, and AI observability. The role also diagnoses and enhances mainframe, Java/Spring, GCP, and Splunk-based systems, while authoring technical documentation and improving autonomous agent performance.

Lead modernization of enterprise applications and systems through cloud architecture, generative AI software development practices, and software engineering automation. Design, build, test, deploy, and maintain scalable cloud solutions while addressing availability, performance, security, and cost. Establish engineering standards, resolve complex technical issues, advise stakeholders, mentor colleagues, and collaborate with infrastructure, operations, security, and senior leadership teams.