Top AI Engineer Jobs

Deploy and support industrial AI and digital manufacturing solutions across manufacturing sites. Translate plant needs into system requirements, coordinate implementation partners, manage data and equipment readiness, integrate AI systems with SAP, MES, EWM, and factory equipment, execute testing and deployment, and provide operational handover and troubleshooting. The role supports machine vision inspection, digital twins, edge computing, performance monitoring, and ongoing model or configuration updates.

Lead the technical direction, development, evaluation, security, observability, and cost optimization of production LLM agent systems. Build shared agent infrastructure, improve reliability and fault tolerance, establish testing and rollout standards, and partner with engineering, product, research, and security teams. Mentor engineers, support AI coding-agent adoption, investigate incidents, and guide model selection and production readiness across teams.

Build and operate production-grade agentic AI systems for enterprise SaaS, including orchestration, MCP tools, memory, RAG, knowledge retrieval, security guardrails, evaluation, and observability. Own products from prototyping through production, make tradeoffs among accuracy, latency, reliability, and cost, support incident response, create reusable tooling, and mentor engineers. The role requires expert Python, cloud-native infrastructure, distributed systems, and hands-on LLM production experience.

Build and lead production AI engineering solutions for client engagements, including agentic systems, RAG pipelines, vector retrieval, MCP integrations, evaluations, observability, and guardrails. Partner with architects and consultants, coach client engineering teams, and take AI systems from prototype through monitored production deployment. The role also contributes to internal AI products and reference architectures.

Build and deploy production AI features including LLM workflows, RAG systems, agents, classification, extraction, and machine learning models. Own ingestion, embeddings, indexing, retrieval, evaluation, monitoring, API integration, responsible AI, and production reliability. Collaborate with product, design, data, platform, and client stakeholders; explain technical tradeoffs clearly. Contribute to reusable AI standards and architectures while rapidly prototyping and hardening successful solutions.

Design, build, deploy, and operate production-grade AI agents and generative AI solutions across enterprise products. Develop Python-based AI systems, RAG pipelines, multi-agent workflows, evaluation frameworks, observability, safety controls, and MLOps processes on cloud infrastructure, especially Azure. Integrate AI into enterprise platforms, optimize production performance and costs, support digital twins, and collaborate with engineering, data, product, and technical teams in agile environments.

Lead the architecture and development of an internal AI developer platform. Build cross-platform onboarding, telemetry, secure sandboxing, AI agent integrations, and provider-agnostic abstractions. Establish engineering standards, review code and architecture, support production incidents, evaluate emerging AI technologies, and shape the platform roadmap. Mentor senior engineers and collaborate with platform users and stakeholders in a regulated financial-services environment, with emphasis on security, observability, reliability, and agent evaluation.

Builds infrastructure, evaluation systems, tooling, and workflows for AI-powered software development. The role focuses on background agents, agent performance measurement, evaluation sets, feedback loops, developer adoption, code review improvements, reliability, observability, and safe autonomous engineering workflows. It requires hands-on software engineering, technical leadership, product judgment, collaboration with internal users, and the ability to deliver ambiguous, dependency-heavy projects.

Build autonomous AI agents that translate proven attack paths into targeted security-control changes, safely apply or stage those changes, and verify remediation by rerunning attacks. Responsibilities include ML reasoning systems, evaluation and training data pipelines, counterfactual experiments, heterogeneous control-plane integrations, safety architecture, staged rollouts, rollback, auditability, and diagnosing agent failure modes. The role requires production ML engineering, agentic systems or LLM post-training, outcome-based evaluation, Python software engineering, distributed systems, and cloud infrastructure experience.

Leads global red team and AI security operations, including advanced attack simulations across network, endpoint, application, cloud, physical, and AI environments. Directs adversarial testing of LLMs and AI-enabled applications, develops offensive tooling and methodologies, evaluates identity and cloud attack paths, and translates findings into enterprise risk reduction. The role also establishes operating standards, mentors red team operators, supports executive reporting, and validates remediation efforts.

Principal AI Engineer responsible for designing and governing AI/ML platforms, multi-tenant services, LLM integrations, reusable developer tooling, and secure deployment patterns. The role combines hands-on software engineering, cloud architecture, AI governance, security, CI/CD, infrastructure-as-code, and technical leadership. Responsibilities include establishing AI productization standards, mentoring engineers, partnering with security and compliance teams, and translating complex technical decisions for executives and cross-functional stakeholders.

Designs and delivers production-grade AI systems, including RAG pipelines, embedding and retrieval workflows, evaluation systems, APIs, cloud-native deployments, observability, and performance optimization. Supports secure AI services across their lifecycle, including deployment, monitoring, incident response, prompt management, and responsible AI safeguards. The role requires production AI/ML experience, a bachelor’s degree, and the ability to obtain TS/SCI clearance for federal mission environments.

Own the architecture and hands-on development of production ML and LLM capabilities across an internal product portfolio. Build and evaluate models, multi-tenant AI services, security controls, monitoring, and reusable engineering patterns. Lead AI architecture decisions, mentor senior engineers, advise executives, and collaborate with product, security, legal, and compliance teams. The role is primarily hands-on, remote within the U.S. or hybrid in New York City, and offered as a 12-month contract.

Assess, secure, and improve AI-enabled systems through secure architecture design, AI red teaming, vulnerability identification, adversarial testing, and mitigation development. Evaluate LLMs, RAG systems, AI agents, and multi-agent workflows; build security testing automation and tooling; implement controls; adapt open-source frameworks; and advise clients on AI security, governance, resilience, and responsible deployment.

Build and deploy production-grade generative and agentic AI applications using Python or JavaScript/TypeScript, LangChain, LangGraph, RAG, vector databases, Docker, Kubernetes, and cloud platforms. Develop workflows, integrations, evaluation pipelines, observability, security controls, and CI/CD processes. Optimize quality, performance, reliability, and cost while supporting production operations. Collaborate with architects, engineers, security teams, customers, and stakeholders; create documentation, participate in reviews, mentor engineers, and communicate technical tradeoffs.

Design and deploy production AI/ML systems, including data pipelines, RAG systems, agentic workflows, model integrations, APIs, observability, evaluations, guardrails, and cost optimization. Collaborate with product, design, and client stakeholders while contributing to architecture, code reviews, testing, deployment, and technical strategy. Build reliable, secure, scalable systems using modern LLM and cloud technologies.

Designs, builds, deploys, and monitors enterprise AI applications, APIs, RAG pipelines, predictive model integrations, and automated workflows. Develops secure, evaluated, and explainable AI services using governed data and cloud technologies. Collaborates with engineers, analysts, architects, product owners, and business stakeholders to translate models into practical decision-support tools while implementing responsible AI, privacy, access controls, testing, monitoring, and operational documentation.

Develops and deploys AI-enabled applications across internal technology teams. Responsibilities include monitoring emerging AI technologies, identifying use cases, rapidly prototyping solutions, integrating models with APIs and data pipelines, and guiding production deployment. The role owns AI initiatives end to end, evaluates accuracy and trustworthiness, improves reusable platform components, and delivers workshops on AI best practices. Strong full-stack, cloud AI, stakeholder management, and continuous learning skills are required.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

Designs, develops, deploys, and supports production AI agents using the Claude Agent SDK and AWS Bedrock AgentCore. Builds agent workflows with Python or TypeScript, tool calling, MCP integrations, and API orchestration. Creates GitHub Actions CI/CD pipelines, configures AWS IAM, ECR, and Docker deployments, and implements secure authentication, logging, monitoring, and observability. Troubleshoots and optimizes production performance while supporting client workshops and meetings.

Develop and deploy learning-based robotic manipulation systems, connecting perception, policy models, sensors, inference, and robot control. Fine-tune vision and action models, optimize inference for physical hardware, implement responsive policy execution, diagnose failures across software and hardware, and build reusable robot software with logging, replay, evaluation, configuration, and testing capabilities.

Build and deploy production-ready AI, machine learning, and generative AI models, pipelines, microservices, and multimodal systems. Develop data ingestion, ETL, vector and graph database integrations, APIs, user interfaces, and distributed infrastructure across air-gapped, hybrid, and on-premises environments. Use Kubernetes, GPU infrastructure, MLOps, and cloud technologies while supporting governance, security, data lineage, and AI compliance. Provide technical mentorship and drive engineering standards.

Build and deploy production AI, machine learning, generative AI, and multimodal systems, including models, pipelines, microservices, vector and graph databases, ETL workflows, and LLM applications. Develop application interfaces and backend services using Python, TypeScript, React, FastAPI, and related technologies. Support Kubernetes, distributed, hybrid, air-gapped, GPU, and cross-domain environments. Ensure AI governance, security, documentation, and data lineage while mentoring engineers and standardizing practices.

Design and productionize AI-powered data solutions on Snowflake using Cortex AI, Cortex Code, Snowpark, RAG, vector search, and agentic workflows. Build scalable data platforms, ELT/ETL pipelines, and real-time integrations with Kafka, Spark, Airflow, and dbt. Lead customer workshops, architecture reviews, proofs of concept, and deployments while developing reusable enterprise AI patterns and accelerators.

Build and lead production AI agents, automations, integrations, and reporting systems for paid media, creator, affiliate, CRO, and ecommerce operations. Integrate major marketing platforms, automate campaign management and budget optimization, connect spend to attributed revenue, and establish engineering standards. Partner with marketing teams, review and mentor builders, drive adoption, manage automation priorities, and report measurable business impact to leadership.