Top AI Engineer Jobs

Optimize production AI workloads on Arm edge devices for performance and power efficiency. Develop kernel- and system-level implementations for DNN models using AI optimization tools and profiling techniques. Create production reference implementations and technical documentation, diagnose complex customer performance issues, and collaborate across engineering teams. Translate technical findings into recommendations for engineers and senior leadership while influencing Arm’s IP and software roadmaps.

Design, build, and operate autonomous AI agents that automate SRE, DevOps, and DBA workflows. Develop safe agent architectures with planning, tool use, error recovery, evaluation, auditability, and human escalation. Integrate agents with Kubernetes, Terraform, cloud APIs, databases, monitoring platforms, and CI/CD systems. Partner with infrastructure teams to identify automation opportunities and shape Airwallex’s infrastructure AI strategy.

Build and deploy AI systems for life sciences research institutions. Partner with scientific and engineering teams to integrate Claude into research workflows, develop MCP servers, benchmarks, agent skills, and domain-specific infrastructure, and identify deployment challenges involving heterogeneous data and auditability. Create documentation and technical content that enables partners to self-serve and scale successful solutions across the life sciences ecosystem.

Design, evaluate, and deploy LLM-powered features end-to-end: prompt engineering, semantic retrieval, embeddings, vector stores, evaluation frameworks, and production integration while collaborating with product and engineering and working directly with users.

Design, deploy, and scale responsible AI and machine learning solutions supporting healthcare provider experiences. Lead AI initiatives, provide technical direction to AI engineers, evaluate emerging technologies, and partner with product, data, and business teams. Ensure models meet enterprise standards for performance, security, governance, and scalability while contributing to cloud modernization. The role emphasizes generative AI, LLMs, conversational AI, API development, cloud hosting, and production operations.

Leads enterprise AI/ML architecture and engineering strategy, developing scalable AI-powered applications, agentic and generative AI solutions, streaming data pipelines, and cloud-native platforms. The role uses Python, Java, Spring Boot, Next.js, Kafka, and PostgreSQL while establishing architecture standards, governance, security, and reliability practices. Responsibilities include technical leadership across programs, stakeholder influence, mentoring engineering teams, evaluating emerging technologies, and driving responsible AI adoption.

Designs, operates, and improves enterprise AI platforms and integrations across Azure, Microsoft 365, Power Platform, and related technologies. Establishes governance, security, identity, deployment, observability, cost-management, and production-support standards for AI agents and generative AI solutions. Leads technical initiatives, develops reusable platform patterns, integrates enterprise systems, mentors engineers, and partners with cybersecurity, identity, architecture, legal, finance, and business teams to enable secure and reliable AI adoption.

Design and own developer infrastructure for isolated, production-like testing environments. Build CI/CD pipelines, deployment automation, build systems, realistic data and behavior simulation, and infrastructure supporting integration, load, performance, and correctness testing. Enable AI coding agents to test and validate code autonomously while improving engineering velocity, reliability, scalability, and cost efficiency. Partner closely with engineering teams to eliminate development bottlenecks.

Designs and scales agentic AI, automation, data engineering, and cloud solutions for sustainability challenges. Builds trusted data models, pipelines, APIs, quality controls, predictive analytics, and intelligent workflows supporting ESG reporting and environmental decisions. Experiments with emerging AI technologies and deploys secure, reusable, production-ready capabilities using modern DevOps and CI/CD practices while collaborating with sustainability, product, data, architecture, and engineering teams.

Develop scalable AI-powered solutions using Salesforce Agentforce, Data 360, Apex, Lightning Web Components, and adjacent AI technologies. Responsibilities include building agents and automations, designing data architectures, prototyping emerging AI capabilities, integrating third-party systems, collaborating with customers and project teams, and translating complex requirements into secure technical solutions. The role also involves maintaining Salesforce certifications, staying current with AI advancements, and traveling up to 25%.

Build and enhance Salesforce Agentforce agents, workflows, automations, and Data 360 architectures. Develop Apex, Lightning Web Components, and JavaScript solutions; integrate Salesforce with external systems; prototype emerging AI technologies; and design scalable data foundations. Collaborate with project teams and customers to define and deliver complex solutions, explain technical concepts, maintain Salesforce certifications, and contribute to AI and Salesforce communities. The role includes up to 25% travel.

Develops, trains, optimizes, and deploys AI/ML models and automation tools for U.S. Army enterprise initiatives. Responsibilities include preprocessing large datasets, evaluating model performance, deploying solutions in secure on-premises environments, collaborating with stakeholders and technical teams, using Palantir Foundry and Microsoft Copilot, and documenting AI use cases for nontechnical users.

Build and deploy secure AI applications, agents, and workflow automations using Claude, LLMs, APIs, RAG, and agentic workflows. Partner with Finance, HR, Legal, Security, and Compliance teams to automate business processes, integrate enterprise systems, and establish privacy, governance, monitoring, audit, and human-approval controls. Measure business impact and move AI solutions from prototypes into reliable production applications.

Lead the technical vision for AI agents, strategy optimization, and simulation platforms. Integrate governed agents into the engineering lifecycle, build secure data access and continual-learning workflows, advance optimization capabilities, and mature offline simulation for product experimentation. Translate ambiguous business problems into measurable objectives, define architecture and guardrails, decompose strategy into engineer-owned work, mentor distributed teams, and represent the function with technical partners and vendors.

Develop and deploy AI-powered solutions for Mirion’s Medical business units, including computer vision, agentic AI, and LLM applications. Identify operational improvement opportunities, translate business needs into technical solutions, and own projects from prototyping through production and optimization. Partner with manufacturing, quality, service, engineering, IT, and regulatory teams while ensuring compliance with enterprise standards and medical-industry requirements. Communicate outcomes, support adoption, monitor performance, and deliver measurable improvements in quality, productivity, cycle time, and cost.

Design and architect Android systems and modular components to support multiple apps. Ship end-to-end user features using an AI-assisted workflow, improve developer tooling and CI, set quality and safety standards for AI-generated code, solve performance and production issues, and document playbooks for the team.

Build and deploy AI and machine-learning capabilities for cyber threat hunting, anomaly detection, incident response, detection engineering, and security automation. Develop custom models, LLM applications, and agentic systems, managing the full production lifecycle from data preparation through monitoring. Partner with security, engineering, offensive security, and governance teams to evaluate emerging technologies, support responsible AI, advise leadership, and mentor technical staff.

Lead the maturity and optimization of Google SecOps, including detection rules, parsers, data pipelines, log integrations, and platform administration. Develop AI-assisted security workflows, automations, enrichment capabilities, and high-confidence detection content. Partner with Security Operations, Threat Intelligence, Incident Response, and Engineering teams to improve alert triage, investigations, response times, data quality, and analyst productivity. Track detection effectiveness and maintain technical documentation.

Designs and deploys Vectra AI integrations across AWS, Azure, and GCP. Builds cloud-native microservices, distributed telemetry and ingestion pipelines, security analytics integrations, and infrastructure-as-code deployments. Integrates NDR with SIEM, SOAR, identity, Zero Trust, and logging platforms while optimizing performance, security, and resiliency. Develops dashboards, automation, runbooks, and monitoring strategies for secure cloud environments.

Build and deploy AI agents for logistics and transportation across email, phone, and SMS. Improve model reasoning, fine-tune foundational models, integrate AI tools, and create evaluation frameworks for agent performance. Collaborate with engineering, product, and logistics teams to solve complex operational problems. The role requires Python, cloud AI deployment, model optimization, and familiarity with TensorFlow or PyTorch. A bachelor’s degree in a related field is required.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Build, deploy, test, scale, and optimize machine learning models and AI inference workflows for edge and cloud environments. Develop maintainable software components and APIs, profile performance, document engineering designs and benchmarks, and communicate technical trade-offs across software, machine learning, and hardware teams.

Designs and operates controlled cyber-capability evaluations for frontier AI models and agentic systems. Builds vulnerable applications, cyber ranges, exploit-development targets, and evaluation harnesses; runs experiments; collects telemetry; analyzes model behavior and evaluation artifacts; and documents security findings. The role also develops sandboxing, containment, access, and secrets-management controls while collaborating with red-team operators, researchers, engineers, and Responsible AI stakeholders.

Supports administration, monitoring, troubleshooting, integrations, testing, deployment validation, incident triage, usage monitoring, documentation, and user enablement for enterprise AI platforms. The role maintains connectors, authentication, indexing, permissions, platform health, resource inventories, and compliance records across multiple environments while helping improve reliability and operational efficiency.

Lead development of a unified fleet knowledge model and decision-capture pipeline for autonomous data-center operations. Build data access layers across relational, telemetry, monitoring, and time-series systems; capture labeled operator decisions; negotiate data requirements with source-system owners; and own scaling considerations including vector indexing, schema evolution, retention, and analytics separation. The role requires strong infrastructure-domain modeling, PostgreSQL expertise, and experience handling time-series and event data at scale.