Top AI Engineer Jobs

Leads development of secure, scalable, cloud-native microservices applications for banking payments. Designs, builds, tests, debugs, and reviews production software using Java, React, distributed systems, and modern architectural patterns. Drives automation, resiliency, continuous delivery, technology modernization, and engineering best practices while mentoring colleagues and contributing to cross-team communities of practice.

Leads software engineering and quantitative development for asset management technology, building secure, scalable systems supporting research, portfolio management, and investment workflows. Develops and reviews production code using Java and/or Python, applies AWS cloud-native practices and infrastructure as code, improves operational stability, and promotes responsible AI-assisted engineering. Partners with stakeholders and vendors on architecture and technical solutions while supporting data engineering, distributed processing, databases, and financial services applications.

Designs and deploys enterprise AI systems centered on large language models, including fine-tuning, retrieval-augmented generation, prompt engineering, autonomous agents, and NLP applications. The role integrates AI into internal tools and digital products, evaluates frameworks, builds scalable training and deployment pipelines, leads architecture decisions, applies emerging research, and mentors team members.

Develop and operate autonomous AI agents and enterprise integrations using Databricks, Claude, MCP, and event-driven architectures. Build Databricks Apps, maintain MCP connectors, monitor agent performance, manage incidents, and document workflows, triggers, dependencies, and data sources. Partner with business, governance, and process teams to identify suitable agentic AI applications, particularly for go-to-market systems, while applying logging, auditing, naming, and change-management standards.

Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Run experiments across prompts, models, routing, caching, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering and manage technical direction and team priorities.

Develop and integrate AI/ML solutions for geospatial intelligence, including spatio-temporal modeling, data fusion, semantic enrichment, trajectory analytics, and remote sensing. Engineer multimodal data pipelines, evaluate models, prototype applications, and implement solutions across hardware and software stacks. Provide technical expertise, mentorship, strategic guidance, and client communication for government mission programs.

Leads development of production-grade AI/ML solutions, including generative AI, RAG, agentic workflows, conversational AI, and predictive models. Designs, evaluates, deploys, and improves secure, scalable services while implementing responsible AI controls, observability, testing, and CI/CD practices. Integrates AI with enterprise data, APIs, cloud platforms, and digital channels; mentors engineers and drives initiatives from proof of concept through production.

Leads the design, integration, and delivery of production-grade AI, generative AI, agentic AI, and MLOps solutions for clients. Serves as technical lead, translates requirements into scalable architectures, mentors engineers, manages technical risks and timelines, supports sales proposals, and presents recommendations to stakeholders. Responsibilities include application integration, security, observability, evaluation, model serving, documentation, testing, and operational readiness.

Designs, implements, validates, benchmarks, troubleshoots, and supports accelerated compute and high-performance storage infrastructure for enterprise AI workloads. Conducts discovery, migrations, installations, configuration, testing, documentation, and client knowledge transfer. Collaborates with project managers, architects, sales teams, partners, and stakeholders to manage scope, timelines, risks, and deliverables. The role requires client-facing consulting, technical documentation, infrastructure troubleshooting, occasional overtime, and travel to client sites or company locations.

Implements, validates, troubleshoots, and optimizes high-performance AI network fabrics supporting GPU clusters and distributed workloads. Configures InfiniBand, Ethernet, RoCE, routing, congestion management, telemetry, cabling, and fabric-management systems. Performs installation, burn-in, performance testing, documentation, knowledge transfer, and client handoffs. Contributes to statements of work, implementation plans, and solution validation while managing project scope, schedules, budgets, and customer communication.

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.

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, 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.

Architect and build production AI security systems spanning endpoint sensors and cloud-scale infrastructure. Lead autonomous cyber defense capabilities, including detection, threat hunting, response, adversary simulation, secure agent execution, and model evaluation. Establish technical direction, benchmarks, and safety practices while mentoring senior engineers and researchers. Carry frontier AI research into the Falcon platform and represent the company through publications, open source, standards, and industry collaboration.

Develop and deploy generative AI assistants, agents, tooling, and software using Python and cloud technologies. Build CI/CD pipelines, deploy AWS services with CloudFormation, and use frameworks including LangChain, LangGraph, Semantic Kernel, and CrewAI. Develop FastAPI services, collaborate with data scientists to productionize analytics solutions, and support machine learning pipelines with Apache Airflow. Serve as a technical lead on complex projects and contribute to software architecture, testing, scalability, and technical documentation.

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.

Leads enterprise AI and software engineering modernization initiatives, designing, building, testing, deploying, and maintaining scalable cloud solutions. Establishes engineering standards, applies generative AI development practices, reviews complex architectures, resolves technical issues, and collaborates with infrastructure, security, operations, leadership, and external partners. Provides technical influence, mentorship, and strategic guidance while supporting cloud migration and automation across the organization.

Build and implement AI-powered developer productivity tools, including code review automations, AI personas, reusable prompts, and workflow playbooks. Partner with engineering teams to identify coding workflow improvements, prototype tools in Python, integrate GitHub and CI/CD systems, measure adoption and quality impact, and support enablement through demonstrations, office hours, and training.

Design, build, test, and release secure shared services, libraries, pipelines, APIs, and platform capabilities for data and AI products. Partner with product managers, evaluate technologies, create design proposals, develop reusable versioned interfaces, and support production operations. Contribute to architecture and roadmap decisions, conduct code and design reviews, mentor engineers, and establish engineering practices for a new team.

Develops, tests, evaluates, and deploys machine learning models using established architectures and frameworks. Supports feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborates with engineering and product teams on AI requirements, researches methods to improve model performance, and supports urgent IT incidents. The role requires strong Python and machine learning experience, knowledge of deep learning and generative AI, and advanced quantitative education.

Develop, test, and deploy machine learning models; support feature engineering, preprocessing, evaluation, and automated training and monitoring pipelines. Collaborate with engineering and product teams to define AI solutions, research methods to improve performance, and support urgent IT escalations. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

Build, test, deploy, and evaluate machine learning models using established frameworks. Support feature engineering, data preprocessing, automated training and monitoring pipelines, and production AI integration. Collaborate with engineering and product teams to define AI requirements, research performance improvements, and respond to critical IT incidents. The role requires advanced quantitative education, 5+ years of AI/ML engineering experience, Python expertise, and knowledge of deep learning and generative AI.

Develop, test, deploy, and serve machine learning models and agentic AI capabilities. Build feature engineering, experimentation, training, monitoring, and REST-based model-serving workflows. Collaborate with data science, engineering, product, and MLOps teams to productionize AI solutions at enterprise scale using distributed computing, Databricks, cloud platforms, and containerized infrastructure. Support model performance improvements, generative AI initiatives, and urgent IT escalations.

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.