Top Artificial Intelligence Jobs

Lead end-to-end data engineering workstreams, building reliable cloud pipelines, governed AI and retrieval workflows, trusted data models, event-driven systems, and automated recovery. Own production reliability, security, observability, cost optimization, testing, audit evidence, and operational readiness. Use Python, advanced SQL, coding agents, machine learning, and defensive AI engineering to deliver secure, resilient platforms while mentoring engineers and translating business needs into practical solutions.

Own discovery, development, deployment, adoption, and optimization of AI agents and automated workflows for internal teams. Build solutions with Microsoft Copilot, Copilot Studio, SharePoint, and Teams; translate business workflows into requirements; measure impact; train stakeholders; and support responsible AI governance. Partner with engineering, IT, HR, and department leaders while serving as the organization’s AI enablement expert.

Advises on enterprise AI security across the model and agent lifecycle. Responsibilities include AI-BOM and AI-SPM governance, pre- and post-production model scanning, CI/CD security gates, AI asset inventory, SaaS agent security, Shadow AI discovery, threat modeling, remediation tracking, application-level troubleshooting, and documentation of security policies, standards, and runbooks.

Own runtime protection for agentic and AI traffic by deploying and configuring AI gateways, Prisma AIRS, Google Model Armor, DLP controls, and Koi integrations. Define policies for agent personas, prevent sensitive-data exfiltration, address OWASP risks, and embed security controls in agents and MCPs. Troubleshoot application-level incidents, support production deployments, and document architectures, policies, and runbooks while advising multiple teams.

Conduct AI red-team testing of connectors and platforms using AI security harnesses and Prisma AIRS. Assess compliance with hardening requirements, perform threat modeling, analyze results, create dashboards, document findings, and provide remediation guidance. Partner with engineering teams and business units to explain risks and drive issue resolution across the AI security program.

Build and maintain backend services and APIs powering LangSmith’s AI observability, tracing, monitoring, and evaluation workflows. Design scalable architectures, optimize storage and query performance for high-volume data, ensure reliability through testing and monitoring, troubleshoot production issues, and collaborate with cross-functional teams on new features and technical documentation.

Build and operate enterprise AI infrastructure, including LLM integrations, automation, observability, deployment pipelines, agentic workflows, vector databases, and RAG systems. Troubleshoot complex production issues, optimize reliability, performance, token usage, and costs, and develop scalable self-service tooling. Partner with engineering, product, business systems, and AI strategy teams to improve internal AI capabilities.

Build and maintain AI-powered solutions, automated workflows, scripts, integrations, and data processes using Python, SQL, PowerShell, APIs, and workflow platforms. Apply AI and LLM technologies to document processing, information retrieval, and business automation. Partner with internal departments, banking vendors, and service providers to improve systems, data quality, reporting, security, and compliance. Monitor production automations, evaluate emerging technologies, document procedures, and deliver measurable operational improvements.

Leads product cybersecurity for AI COE products, coordinating security requirements, threat modeling, risk assessments, vulnerability management, SBOM and third-party software reviews, mitigation activities, and lifecycle documentation. Partners with engineering, ML, architecture, quality, regulatory, and security teams to establish repeatable cybersecurity practices and ensure traceable, compliant releases across AI/ML software products.

Provides strategic AI guidance to national security clients and leadership. Advises on emerging AI capabilities, infrastructure, tools, workforce development, and implementation strategies. Develops and applies AI, machine learning, NLP, mathematical modeling, dashboards, and visualization solutions to support decision-making. Translates complex technical findings for senior stakeholders, facilitates cross-functional collaboration, gathers requirements, and supports mission-critical AI capability development and scaling.

Leads strategy and early product development for agentic commerce and AI-powered payments. Evaluates emerging technologies, customer needs, market opportunities, technical feasibility, and commercial potential. Develops product concepts, requirements, experiments, pilots, and proofs of concept while partnering with Engineering, Product, Network, Risk, Legal, Compliance, commercial teams, technology providers, startups, and ecosystem partners. Translates validated opportunities into platform capabilities, roadmaps, and partner opportunities within a regulated financial services environment.

Design, develop, deploy, and optimize production AI systems, including foundation model training, LLM inference, agentic workflows, vector search, guardrails, evaluation, governance, and observability. Lead multi-model orchestration and cost-performance optimization across cloud infrastructure. Establish AI engineering standards, guide technical roadmaps, lead design reviews, and mentor senior AI engineers while integrating research advancements into scalable, responsible products.

Leads enterprise cloud and AI cyber defense across AWS, Azure, GCP, and OCI. Defines security strategy, telemetry, logging, detection, monitoring, incident response, governance, and compliance controls. Partners with infrastructure, application, engineering, and security operations teams; develops threat-monitoring use cases; evaluates vendors; supports FedRAMP architectures; advises executives; trains SOC analysts; and mentors junior staff. Requires a bachelor’s degree or equivalent background and at least 10 years of relevant systems administration and information security experience.

Partners with account executives to lead technical sales strategy, customer discovery, product demonstrations, workshops, and proof-of-value evaluations. Translates customer goals into Datadog solutions, develops technical champions, manages evaluation timelines, provides product feedback, documents opportunity progress, and collaborates with Product, Support, and go-to-market teams to resolve technical blockers and drive adoption.

Own end-to-end development of production-grade AI-enabled applications across React and TypeScript frontends, Python and Node.js backends, PostgreSQL data models, cloud infrastructure, and CI/CD. Translate complex insurance compliance requirements into intuitive software, maintain platform reliability, optimize performance and accessibility, validate AI-generated code, and collaborate with product, design, and business stakeholders. Mentor engineers and drive technical planning, documentation, testing, deployment, and continuous optimization.

Build and ship production AI features for an insurance compliance platform. Responsibilities include developing LLM pipelines, retrieval and agent integrations, document data extraction, evaluation systems, automated quality checks, and AI-powered assistants. The role uses Python/FastAPI, React/TypeScript, and PostgreSQL while emphasizing security, reliability, maintainability, and rapid prototyping. The engineer will own features end to end, investigate complex customer documents and edge cases, and communicate AI tradeoffs to business and product partners.

Develop algorithms and high-performance systems across the GenAI lifecycle, including multimodal data preparation, distributed foundation-model training, reinforcement-learning post-training, inference optimization, and open-source AI infrastructure. The role involves improving scalability, efficiency, deployment cost, and model support while collaborating with research, product, and infrastructure teams.

Build and own production supply-chain decision-intelligence applications on Palantir Foundry and AIP. Translate planner expertise into governed data pipelines, Ontology models, operational interfaces, analytical functions, AI agents, and tested business rules. Integrate enterprise systems and language models, establish evaluation and human-in-the-loop workflows, measure operational outcomes, and lead design, integration testing, UAT, and production support.

Design and implement enterprise data architectures supporting batch and real-time analytics, ETL/ELT, data warehouses, streaming pipelines, CDC, and cloud data platforms. Build AI-ready data infrastructure for RAG, vector search, and Generative AI applications. The role requires production experience with Treasure Data/CDP, Kafka, Snowflake or Databricks, cloud services, and modern AI data engineering technologies.

Develop and own enterprise-grade agentic AI systems for supply chain operations, including multi-agent architectures, orchestration, RAG pipelines, deployment, monitoring, and lifecycle management. Translate business requirements into technical designs, lead integration testing and design reviews, and define human-in-the-loop workflows. Integrate enterprise data sources, establish observability and evaluation frameworks, and make architectural decisions across cloud, on-premises, hybrid, and containerized environments.

AI internship supporting Zebra’s Solutions Startup team by integrating data sources, building authenticated connectors, orchestrating ingestion workflows, developing Claude-driven analysis commands, and optimizing prompts. The intern will create Python automation, maintain metadata and schemas, update dashboards, generate HTML/PDF briefs, document APIs, and audit system quality. The role also provides mentorship, professional development, and hands-on exposure to AI-enabled business venture analysis.

Leads the strategy, roadmap, discovery, prioritization, delivery, adoption, and measurement of AI-enabled products and workflows. Partners with business, engineering, IT, security, data, and executive stakeholders to translate needs into scalable solutions. Uses rapid prototyping and AI tools to evaluate concepts, while embedding privacy, security, responsible AI, access controls, and human oversight. Establishes product quality standards, success metrics, change-management practices, and repeatable AI product processes.

Own the strategy, roadmap, prioritization, and delivery of AI-enabled products for enterprise functions. Identify high-value opportunities, develop business cases and requirements, assess data readiness and risks, guide solutions from discovery through production, and measure business outcomes. Partner with business and technical stakeholders while leading adoption through communication, training, documentation, and feedback.

Leads AI initiatives and serves as the technical contact for a federal customer while developing production AI/ML solutions in Python. Designs data pipelines, ETL/ELT processes, database schemas, SQL queries, and stored procedures. Builds Azure-based data environments and integrates backend systems with Power Apps and Power Automate. Translates stakeholder requirements into scalable AI and data solutions, with potential work involving generative AI, RAG, and LLM orchestration.

Leads the identification, prioritization, development, adoption, and measurement of AI-enabled solutions across enterprise functions. Partners with business and technical stakeholders to define requirements, manage roadmaps and backlogs, assess data readiness and risk, develop business cases, guide solutions from discovery through production, and drive organizational adoption through communication, training, and feedback.