Top AI Engineer Jobs

Leads enterprise AI tooling and security strategy, building secure cloud-native platforms and protecting AI models, agents, prompts, data, APIs, and outputs. Designs controls for identity, access, encryption, monitoring, threat detection, adversarial testing, governance, and incident response. Provides distinguished technical leadership, mentors engineers, establishes engineering standards, and partners across architecture, security, governance, risk, compliance, and business teams.

Secure Hi Marley’s internal AI environment by threat-modeling LLM integrations, agents, MCP connectors, and data pipelines; enforcing isolation, least privilege, and data boundaries; conducting AI red-team testing; managing AI tool governance; maintaining incident response runbooks; and producing SOC 2 and ISO 42001 compliance evidence. Partner with AI Operations and IT on access controls, cloud workload isolation, DLP, monitoring, and safe AI usage guidance.

Build and operate Crunchyroll’s AI platform, including automated evaluation, governance, telemetry, policy enforcement, reusable RAG and agent components, and enterprise AI tool administration. Develop business-user agents, manage approved AI tool adoption, integrate security controls, and own platform features end-to-end. Partner across engineering, IT, security, and AI teams while establishing scalable engineering standards and golden paths.

Designs, builds, evaluates, deploys, and maintains production-grade agentic AI systems for business teams. Responsibilities include workflow analysis, AI solution selection, architecture, orchestration, RAG, tool integration, reliability engineering, observability, guardrails, stakeholder management, and post-handoff sustainability. The role requires translating ambiguous business needs into practical technical solutions while managing cost, latency, safety, and operational risks.

Design and build security foundations for AI products, including agent runtimes, tool permissions, retrieval, content writes, observability, and auditability. Create reusable security libraries, guardrails, test fixtures, and automated red-team testing for prompt injection, data exfiltration, tool misuse, cross-tenant leakage, and unsafe mutations. Translate threat models into engineering requirements and launch criteria, and continuously harden systems using production signals and incident learnings.

Leads cybersecurity compliance and AI governance across NIST, CMMC, EAR, and ITAR frameworks. Maintains SSPs, POA&Ms, audit evidence, risk assessments, and compliance policies; evaluates AI vendors and use cases; embeds guardrails, access controls, data classification, logging, and review gates into LLM, MLOps, and DevSecOps workflows. Partners with engineering, security, legal, assessors, and government stakeholders to mature organizational compliance and responsible AI practices.

Design and implement application security controls for AI applications, agentic systems, and Vertex AI platform services. Build guardrails against prompt injection, unsafe tool invocation, data exfiltration, and model abuse. Integrate security into CI/CD, software development lifecycles, infrastructure-as-code, and cloud environments. Partner with AI, MLOps, cloud engineering, data science, and model risk teams while developing Python automation, detection capabilities, and reusable security standards.

Lead software engineers and applied AI/ML practitioners while developing secure, scalable production systems in Java or Python. Integrate LLMs, generative AI, NLP, RAG, and coding assistants into software engineering workflows. Establish responsible AI governance, validation standards, testing, security, and operational practices. Guide system architecture, experiments, algorithm implementation, productionization, code quality, automation adoption, and technical decision-making across global teams.

Develops secure, scalable full-stack applications for Asset Management. Responsibilities include software design, coding, testing, troubleshooting, architecture, data analysis, visualization, and operational stability. Uses enterprise-authorized AI coding tools responsibly to improve development productivity while validating outputs for security and correctness. Builds services with Hibernate, Spring JPA, PostgreSQL, REST APIs, and GraphQL, with preferred experience in Kafka, Avro, React, and AWS.

Leads full-stack software engineering within an agile Asset Management team. Develops, reviews, tests, debugs, and operationalizes secure production code using Java, Python, AWS, Spring JPA, Hibernate, PostgreSQL, REST, and GraphQL. Drives adoption and governance of AI-assisted engineering practices, validates AI outputs, mentors technical teams, influences architecture and product decisions, and promotes SDLC automation, reusable frameworks, scalability, resiliency, and security.

Lead design, build, and operate AI-driven automation and infrastructure tooling. Develop secure production code, deploy on Kubernetes, define observability/SLOs, drive AI-assisted engineering adoption, and lead cross-team technical decisions.

Lead architecture and implementation of a patient-controlled health data platform: custody, consent management, secure EHR integrations, AI-native workflows (LLMs, agents, RAG), mobile-first patient experiences, and build the engineering culture and hiring bar as a founding technical partner.

Design, train, deploy, and optimize deep learning and computer vision models for semiconductor inspection systems. Develop production-quality algorithms combining deep learning with physics-based modeling or signal processing, manage datasets and evaluation pipelines, and improve inference performance using GPU and high-performance computing. Collaborate across engineering teams and support deployments at customer sites, including occasional international travel.

Design and implement large-scale, distributed AI-driven SD-WAN systems and real-time, fault-tolerant services. Drive features end-to-end from design through testing and deployment, improve scalability and operability, participate in code and design reviews, and collaborate with product and documentation teams.

Build production-ready agentic AI applications that integrate agents, workflows, memory, reasoning, and tools to solve customer use cases. Rapidly prototype, iterate, and ship end-to-end AI experiences; design integrations and UX, ensure reliability, performance, and evaluation; partner with platform and product teams to create reusable components and customer-ready solutions.

Design, build, and evaluate conversational AI agents using Go and Python. Own agent orchestration, sub-agent architecture, prompt engineering, tool integrations, guardrails, approval flows, observability, and evaluation infrastructure. Develop datasets, scorers, regression suites, and quality metrics for production readiness. Collaborate cross-functionally on conversational experiences, make architecture decisions, rapidly prototype agents, harden them for launch, and mentor engineers.

Build and deploy LLM-powered tools and agents for life sciences research institutions. Partner with scientific and engineering teams to integrate AI into research workflows, develop MCP servers and reusable agent skills, create scientific benchmarks, address data and auditability challenges, and produce documentation enabling partner self-service and global scalability.

Senior AI Engineer will design and build cloud-native, agentic AI applications and platform patterns on AWS. Responsibilities include backend services (Python/FastAPI, TypeScript/Node), agent orchestration, retrieval and knowledge architectures, CI/CD and IaC, security and observability, and technical leadership to drive MVPs to production within agile AI Accelerator cycles.

Lead design and delivery of production-grade agentic AI systems: architect LLM integration, orchestration, and routing; ensure reliability, low latency, cost efficiency, and Responsible AI guardrails; translate AI capabilities into enterprise product value and mentor cross-functional teams.

Design, build, and deploy scalable ML models and agentic AI workflows using foundation models. Develop, test, and monitor multi-step agents, RAG systems, and prompts; integrate agents with enterprise APIs and data; define agent boundaries, benchmarks, and safety; mentor data scientists and document architecture.

Leads delivery of production LLM and AI systems, including multi-provider integrations, RAG, vector databases, agents, MCP tool use, guardrails, evaluation, observability, latency optimization, and cost management. Establishes organizational AI engineering standards, troubleshoots critical features hands-on, manages offshore delivery, reviews designs and code, and reports risks and progress to engineering leadership.

Build and operationalize AI-driven semantic solutions using ontologies, knowledge graphs, controlled vocabularies, APIs, MCP tools, agents, and reusable software components. Enable inference, search, normalization, entity resolution, and data integration across enterprise platforms. Collaborate with platform, product, data, ontology, and governance teams; embed FAIR data principles, responsible AI, security, compliance, documentation, and CI/CD practices into scalable services.

Develop AI agents that improve productivity across HR, Finance, Product Support, Customer Service, and CAD Design. Build and integrate Retrieval-Augmented Generation models, fine-tune open-source models, collaborate with cross-functional stakeholders, integrate AI into existing systems, and troubleshoot and optimize model performance.

Design, develop, and deploy AI solutions in production; lead AI pilot programs; integrate AI capabilities into existing systems; provide technical guidance; contribute to full-stack development; and optimize AI models for performance and scalability. The role requires expertise across programming, cloud platforms, APIs, databases, SAP, deep learning, NLP, computer vision, and DevOps, supporting financial auditability and transportation logistics for USTRANSCOM.

Lead architecture and delivery of an ML infrastructure efficiency platform across silicon co-design teams. Define platform contracts, onboarding, orchestration, auth, observability, storage/caching, and SLA enforcement. Own production operation, security, reliability, and performance while mentoring engineers and driving cross-team technical decisions.