Top Artificial Intelligence Jobs

Contribute to mechanical design and development for aerospace components. Perform CAD modeling and drawing, FEA simulation (static, fatigue, vibration), sizing, GD&T/tolerance studies, interpret customer requirements, and apply Python for root solving and systems analysis while supporting product development and cross-functional teams.

The Distinguished Engineer will drive innovation in banking technology, lead software engineering projects, mentor teams, and utilize AI and advanced architectures for risk management.

Serve as the Platforms & Services Segment IT leader advising executive leadership on IT strategy, digital transformation, cybersecurity, service delivery, portfolio and financial management, talent development, and demand prioritization to ensure aligned, secure, and outcome-driven IT and digital solutions.

Lead enterprise payments architecture for card-present solutions, provide technical governance and guidance, integrate AI-enabled capabilities securely, develop and review production code, evaluate emerging technologies, and drive architecture decisions across product families.

Design, develop, test, and maintain secure, high-quality production software. Create architecture and design artifacts, analyze large datasets to improve systems, troubleshoot complex problems, and contribute to engineering communities and inclusive team culture.

Lead architecture strategy and roadmaps for cloud-based, AI-enabled payment systems. Advise on technology selection, build reusable secure engineering patterns, develop and review production code, govern APIs and integration patterns, and drive digital transformation for onboarding and self-service experiences.

Support process-based risk assessments across Enterprise Services, identify and mitigate process-level risks, advise stakeholders, research emerging regulatory and technology risks, draft deliverables, manage multiple projects and workstreams, and drive process and controls improvements to strengthen the company's risk posture.

Supports enterprise AI upskilling, change management, communications, and responsible AI adoption across the Commercial & Investment Bank. Responsibilities include delivering AI literacy training, analyzing program gaps, creating data visualizations and executive communications, maintaining engagement metrics, and improving enablement initiatives. The role requires translating complex AI, technical, and regulatory concepts into practical materials while coordinating multiple initiatives across diverse stakeholders in a regulated financial environment.

Lead adoption of agentic AI across product teams by training, coaching, and embedding AI-enabled workflows. Identify high-value use cases, prototype and implement practices, design playbooks and learning paths, measure adoption and outcomes, and partner across product, engineering, design, data, risk, and operations to scale durable, responsible AI-driven operating models.

Contribute to building and hardening micro-apps and AI integrations on a shared GCP platform. Complete bug fixes and small features, participate in code and design reviews, apply security and software development best practices, monitor applications and follow runbooks, and use AI tools to assist development, testing, and documentation while learning from senior engineers.

Leads development and deployment of generative AI solutions, including assistants, AI processing pipelines, tools, and scalable software services. Builds Python and Go applications using FastAPI and Gen AI frameworks, deploys AWS infrastructure with CloudFormation, and manages GitHub Actions CI/CD pipelines. Develops edge-device services, supports machine learning pipelines with Apache Airflow, and collaborates with data scientists to productionize analytics solutions. The role also requires software architecture, testing, technical writing, and coordination of complex cross-functional projects.

Develop and deploy generative AI assistants, processing pipelines, tooling, and software using Python or Go. Build FastAPI services, edge-device components, CI/CD pipelines, and AWS infrastructure. Apply LLMs, prompt engineering, and modern agent architectures using LangChain and LangGraph. Collaborate with data scientists to productionize scalable analytics and AI solutions, and assist with Airflow-based machine learning pipelines. The role also requires software architecture, testing, technical writing, and cross-functional collaboration.

Lead full-stack engineering teams building ServiceNow’s AI Experience Framework, a Lit-based platform for reusable conversational user interfaces. Own roadmap delivery, architecture, technical direction, staffing, mentoring, production escalations, and cross-team coordination. Guide decisions involving AI integration, performance at scale, server-rendered components, routing, APIs, state management, and multi-team framework adoption.

Develop AI-powered contact center and agentic AI solutions using Dialogflow, Amazon Lex, Azure OpenAI, and AWS Bedrock. Design accessible user interfaces, natural language interactions, voice experiences, mock-ups, and prototypes using Figma, HTML5, and AngularJS. Conduct user research and usability analysis, collaborate with multidisciplinary teams, optimize workflows, assess industry trends, and guide junior team members while delivering client-focused AI strategies.

Develops production-grade data products, predictive models, AI applications, pipelines, APIs, dashboards, and multimodal solutions for product cost optimization. Partners with engineering, purchasing, finance, manufacturing, and program teams to translate ambiguous business problems into measurable technical outcomes. Responsibilities include modeling, deployment, monitoring, MLOps, data governance, LLM and RAG applications, document intelligence, and communicating insights to technical and non-technical stakeholders.

Build and ship LLM-powered production features end-to-end: prompt orchestration, tool calling, retrieval/RAG, embeddings, evaluation infrastructure, observability, safety guardrails, and integrations. Collaborate with clinical teams, prototype quickly, measure outcomes, and raise engineering standards through mentorship and technical leadership.

Lead the design and delivery of enterprise AI platform capabilities and production agentic AI solutions. Build multi-agent systems, RAG and knowledge management solutions, cloud-native infrastructure, and MLOps/LLMOps capabilities. Partner with business, architecture, security, legal, and risk stakeholders; influence architecture decisions; mentor engineers; and establish reusable engineering, governance, monitoring, and operational practices.

Build enterprise AI platform capabilities and production AI applications, including agentic workflows, retrieval-augmented generation, semantic search, vector databases, cloud-native services, and AI model operations. Collaborate with senior engineers on orchestration, developer tooling, governance, security, responsible AI, monitoring, testing, and compliance. Use Python, Google Cloud Platform, Kubernetes, CI/CD, and infrastructure as code while developing scalable AI solutions.

Designs and delivers enterprise AI platform capabilities and production-ready agentic applications. Builds multi-agent systems, RAG and knowledge platforms, orchestration frameworks, cloud-native services, and developer tooling. Implements MLOps and LLMOps for deployment, evaluation, monitoring, observability, and optimization. Partners with stakeholders, contributes to architecture and engineering standards, and independently owns initiatives from design through production deployment.

Lead design, build, and productionization of scalable, secure data pipelines and products in Azure Databricks. Implement batch and streaming ETL, Vector Search, RAG, LLM integrations, automation with Cursor/agents, CI/CD, observability, and governance. Mentor peers and partner with data scientists to operationalize AI while owning platform reliability and efficiency.

Build production AI agents and agent-powered features from research and prototyping through deployment. Develop evaluation infrastructure with golden datasets, regression suites, and offline/online scoring. Own retrieval and context pipelines, production instrumentation, failure taxonomies, and cost and latency budgets. Partner with data science and product management on experiments and agent behavior, while establishing LLM engineering patterns, guardrails, and reusable tooling.

Develop and maintain high-performance Linux sensor components in C/C++ to secure containerized cloud workloads. Design and implement eBPF-based features, ensure reliability and performance, debug and test at scale, collaborate across teams, lead container and orchestrator security initiatives, and participate in code reviews and CI/CD practices.

Leads an engineering team developing AI-native cybersecurity products and capabilities. Defines technical vision and architecture for agentic AI, LLM, retrieval, reasoning, and autonomous security solutions. Drives concepts from rapid experimentation through enterprise production while ensuring security, reliability, scalability, governance, and evaluation. Partners with product, UX, researchers, customers, and executives; remains hands-on in architecture and implementation; and builds a high-performing team across AI/ML, distributed systems, and cybersecurity.

Design, build, test, and maintain scalable, secure data pipelines and architectures. Update data models, advise on/configure tools, validate controls, use SQL/NoSQL, and apply enterprise AI capabilities to accelerate and validate data engineering workflows while ensuring traceability and data sensitivity handling.

Lead the design, development, deployment, and support of foundational AI systems, including LLM training and inference, similarity search, guardrails, evaluation, governance, and observability. Develop optimization techniques that improve scalability, cost, latency, throughput, and hardware utilization. Partner with engineering, research, product, and program teams while contributing to AI platform strategy, technical vision, and long-term roadmaps.