Top AI Engineer Jobs

Designs, optimizes, and operates scalable production inference services for enterprise AI and machine learning. Integrates LLMs and intelligent services, develops MLOps pipelines, optimizes latency and throughput, and ensures reliability, observability, security, governance, and compliance. Partners with platform, infrastructure, security, evaluation, product, and application teams while mentoring junior engineers.

Designs, implements, and supports Azure cloud, data, AI, and machine-learning capabilities for enterprise and manufacturing environments. Builds MLOps workflows, operationalizes models, integrates AI with business and process-control systems, applies security and governance controls, automates infrastructure and deployments, monitors service and model health, and troubleshoots operational issues. Collaborates with infrastructure, cybersecurity, application, data, and manufacturing teams throughout the technology lifecycle.

Build and ship AI-enabled software across internal sales tools, client products, and reusable agent components. Scope ambiguous problems, deliver production features with limited oversight, generalize tooling, and collaborate with platform engineering leadership. The role requires taking AI applications beyond prototypes, working directly with clients and stakeholders, applying broad full-stack skills, validating AI-generated code, and communicating effectively. Occasional travel for client kickoffs is required.

Build and evaluate production AI agents, develop reliability and quality evaluation frameworks, improve information storage and retrieval, and convert applied research into measurable production infrastructure. The role includes designing research programs, measuring agent action sequences, optimizing inference cost, and helping guide applied research priorities.

Leads the architecture, development, deployment, and delivery of production AI, machine learning, generative AI, and public health analytics solutions. Translates client objectives into technical requirements, roadmaps, and architectures; guides multidisciplinary teams; manages technical risks; and serves as the primary client liaison. Establishes secure software engineering, MLOps, DevSecOps, responsible AI, privacy, governance, testing, monitoring, and documentation practices. Develops AI applications, RAG workflows, intelligent agents, APIs, and reusable components in cloud environments.

Supports an enterprise sales pod by researching target accounts, identifying stakeholders, creating AI-assisted outbound campaigns, booking qualified meetings, and improving follow-up and opportunity progression. Builds AI agents, workflows, automations, dashboards, and GTM assets to increase account prioritization, personalization, engagement, pipeline, and ARR. Partners closely with enterprise account executives and contributes beyond initial meeting generation.

Build and improve production document-intelligence systems for commercial insurance using parsing, OCR, classification, structured extraction, reconciliation, retrieval, summarization, and deterministic logic. Evaluate model and parser behavior, investigate failures, maintain evaluation sets, compare technical approaches, collaborate with domain experts, and ship reliable Python and Azure Functions services with strong testing, observability, security, cost, and latency considerations.

Build and ship LLM- and agent-based prototypes and production solutions. Develop traditional ML where appropriate, prioritize reuse and scalability, collaborate with product, platform, and frontend partners, and support business development with technical credibility.

Build the core intelligence layer for an AI-native clean energy infrastructure platform. Responsibilities include setting technical strategy, developing LLM-powered agent workflows, building backend features, creating data and geospatial pipelines, automating contracting and diligence, and shipping production software. The role requires autonomous execution, direct customer collaboration, rapid prototyping, technical leadership, and experience with GenAI systems, orchestration frameworks, vector databases, and backend engineering.

Design, develop, deploy, and optimize enterprise AI solutions, including LLMs, NLP, computer vision, multimodal systems, and predictive models. Architect scalable training, inference, and MLOps pipelines; implement RAG, vector database, and agentic AI applications; optimize distributed cloud-based systems; establish model governance, monitoring, security, and responsible AI practices; and mentor engineers while providing technical leadership.

Designs and deploys enterprise conversational AI applications using LLMs, agentic workflows, multi-agent systems, RAG, and prompt engineering. Builds evaluation frameworks, SDKs, APIs, developer tooling, governance, safety controls, and scalable production infrastructure. Partners with cross-functional teams, evaluates emerging AI technologies, optimizes model performance and operations, and mentors engineers while establishing architecture, testing, deployment, and monitoring standards.

Design, fine-tune, optimize, and deploy enterprise-scale large language models and NLP solutions. Build distributed GPU training pipelines, datasets, evaluation frameworks, RAG systems, embedding and vector-search solutions, and MLOps workflows. Lead model safety, benchmarking, inference optimization, architecture reviews, and technical mentoring while collaborating with research, engineering, and product teams.

Build LLM- and agent-based prototypes and production solutions, develop ML models where appropriate, prioritize reuse and scalability, collaborate with product, platform, and front-end partners, and support business development through technical delivery and client-facing engagement.

Architect and implement production AI/ML solutions (including generative and agentic systems), perform advanced analytics and experiments, define model evaluation and lifecycle standards, integrate solutions with engineering teams, and ensure compliance with banking regulations while mentoring peers.

The Principal AI Engineer designs and implements AI systems to solve business challenges, focusing on advanced analytics and collaboration with teams, while ensuring compliance and managing risks.

Develop and deploy GenAI and agentic AI solutions, including RAG pipelines, tool-calling agents, multi-agent workflows, data pipelines, and computer vision applications. Build production-quality Python and API code using cloud platforms, databases, and machine learning frameworks. Monitor model performance, evaluate AI quality, troubleshoot production issues, follow CI/CD and responsible AI practices, and collaborate with product, data, and engineering teams.

Founding Full-Stack AI Engineer to architect, build, and scale an enterprise digital account manager. Own end-to-end features, build integrations and data ontology mapping, develop intuitive UIs, and design agentic LLM solutions (memory, RL). Work directly with founders and customers to iterate rapidly and deliver enterprise-grade, scalable AI products onsite in San Francisco.

Designs and operates governance, security, deployment, monitoring, and cost-management frameworks for enterprise AI platforms. The role builds semantic models in Snowflake and Databricks, develops CI/CD and infrastructure-as-code solutions, enables multi-environment deployments, monitors AI reliability and usage, and implements auditability, responsible AI standards, RBAC, identity controls, and policy enforcement. It also partners with data and engineering teams to manage lineage, governed metrics, model operations, and cloud consumption across AI workloads.

Build and operate AI, MLOps, and deployment pipelines across customer and internal environments. Lead technical integrations, prototypes, demos, and production deployments across cloud, on-premises, and hybrid systems. Debug complex infrastructure and application issues, gather customer requirements, translate field insights into product capabilities, and create technical content. Partner with customers, Product, Engineering, Sales, and Business Development while helping establish forward-deployed engineering standards.

Designs, builds, and operates production-grade agentic AI systems at enterprise scale. Responsibilities include multi-agent orchestration, tool use, planning, memory, retrieval-augmented generation, model integration, evaluation, safety governance, observability, and workflow automation. The role also provides technical leadership through architecture ownership, code reviews, mentoring, and engineering standards for reliable production AI systems.

Build and operate production AI platform capabilities, including agentic and multi-agent systems, evaluation and monitoring frameworks, fine-tuning workflows, inference optimization, safety controls, APIs, and rollback mechanisms. Partner with data and infrastructure teams to deliver reliable, scalable AI services while balancing latency, cost, GPU utilization, and operational risk. Senior engineers also mentor teammates, review designs, and raise engineering standards.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Lead architecture and delivery of complex AI harness and distributed software systems. Design agentic frameworks, AI/ML integrations, full-stack applications, cloud infrastructure, CI/CD pipelines, security controls, and enterprise integrations. Establish testing, performance, token optimization, and anti-hallucination practices; guide technical strategy, mentor engineers, and oversee projects from prototyping through deployment. The role is onsite in McLean, Virginia, includes occasional travel, and supports secure government and defense workflows.

Build and extend backend systems powering enterprise AI capabilities for data modeling, schema generation, documentation, governance, model orchestration, usage metering, and MCP integrations. Develop .NET microservices on AWS, integrate LLM providers, manage context and validation, and create reliable REST APIs for enterprise data workflows.

Leads the design, development, deployment, and support of secure, scalable full-stack applications using Java, Spring Boot, React, and TypeScript. Owns cloud-native microservices, APIs, event-driven Kafka architectures, observability, reliability, and technical architecture decisions. Mentors engineers and establishes quality standards while advancing AI-assisted development, agentic workflows, responsible AI practices, and engineering automation.