Top AI Engineer Jobs

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.

Leads enterprise-scale AI architecture and implementation across infrastructure and agentic AI systems. Defines patterns for model integration, retrieval, inference, observability, evaluation, and safety; resolves complex technical challenges; establishes long-term AI engineering strategy; guides cross-team initiatives; partners with infrastructure, security, risk, and product leaders; evaluates emerging technologies; and mentors senior engineers. The role requires extensive experience building production AI platforms, GPU infrastructure, Kubernetes environments, and governed AI systems in regulated settings.

Build and operate production-grade agentic AI systems, including orchestration, tool use, structured generation, RAG pipelines, evaluation frameworks, observability, and reliable event-driven services. Lead technical design, establish reliability and rollout standards, evaluate emerging models, and mentor engineers. Collaborate across Product, UX, Data Science, architecture, and engineering teams while shaping shared AI platforms and developer tooling for regulated financial-services environments.

Build and operate production-grade agentic AI systems, including LLM-powered customer features, RAG pipelines, tool-enabled workflows, shared AI infrastructure, orchestration, evaluation, and monitoring. Own reliability, latency, cost, security, auditability, and failure recovery in a regulated financial-services environment. Partner with product and design teams to deliver customer-focused web and financial capabilities.

Build and operate production-grade agentic AI services for commercial payments, including orchestration, tool use, structured generation, RAG and embedding pipelines, evaluations, observability, reliability controls, and secure deployment. Collaborate across engineering, product, UX, data science, and architecture teams while contributing to shared AI frameworks and platform tooling. The role requires backend and distributed-systems expertise, strong TypeScript or Go skills, shipped LLM features, and familiarity with event-driven systems.

Build and operate production agentic AI systems for financial services. Responsibilities include developing LLM-powered features, agentic workflows, RAG pipelines, shared AI infrastructure, monitoring, debugging, and reliability improvements. The role involves production coding, system design, collaboration with product and design teams, and responsible use of Claude and Codex while meeting stringent security, compliance, auditability, and reliability standards.

Build and deploy generative AI, agentic systems, and intelligent automation solutions for enterprise business opportunities. Partner with business, product, engineering, risk, and platform teams to discover use cases, validate measurable value, scale prototypes into production, and establish reusable AI capabilities. Responsibilities include software development, workflow integration, evaluation, monitoring, security, responsible AI governance, stakeholder influence, and mentoring. The role requires hands-on engineering, systems thinking, structured AI-assisted development, and occasional travel for workshops or delivery milestones.

Implements, deploys, monitors, and supports machine learning and LLM-based systems in GPU-enabled enterprise environments. Responsibilities include model packaging, validation, versioning, deployment, rollback, CI/CD, container and Kubernetes configuration, observability, testing, troubleshooting, and compliance with AI governance and security standards. The role collaborates with product, technology, and business teams to deliver reliable production AI capabilities and escalates complex issues to senior engineers.

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 and operates production AI systems, including LLM, RAG, agentic, and multimodal applications. Builds evaluation frameworks, automated and human-in-the-loop testing pipelines, MLOps workflows, retrieval pipelines, orchestration layers, and scalable data systems. Defines quality, safety, reliability, latency, and cost benchmarks; integrates evaluation into CI/CD and governance; performs failure analysis; and mentors engineers while partnering with product, engineering, risk, and governance teams.

Build and operate production agentic AI systems, including LLM-powered features, tool-calling workflows, RAG pipelines, shared AI infrastructure, and evaluation tooling. Develop reliable, secure, and auditable systems using Python, Go, or TypeScript across cloud and distributed environments. Collaborate with product, design, and engineering teams to translate customer needs into technical solutions, while monitoring, debugging, and improving deployed AI systems.

Build and operate production agentic AI systems, including LLM-powered features, tool-calling workflows, RAG pipelines, shared AI infrastructure, evaluation tooling, and monitoring. Collaborate with product and design teams to deliver reliable, secure, explainable AI capabilities for financial services. Contribute to system design, production debugging, performance improvements, and customer-focused technical solutions.

Designs, builds, and operates production-grade agentic AI systems and shared capabilities, including orchestration, planning, tool use, memory, retrieval, LLM infrastructure, evaluation, observability, and safety. Provides technical direction, owns cross-team architecture, leads design reviews, evaluates emerging AI techniques, partners with product and UX teams, and mentors engineers. The role requires deep distributed-systems and agentic AI expertise in an enterprise, regulated environment.

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.