Top AI Engineer Jobs

Lead pre- and post-sales engagements to drive enterprise-wide value using the Celonis Process Intelligence platform. Perform technical discovery, design AI/LLM-based solutions (including agentic systems and RAG), manage implementations and value realization, run QBRs and executive cadences, and enable customer adoption through roadmaps, training, and cross-functional coordination.

Lead architecture and delivery of production-grade generative AI systems: multi-agent orchestration, multimodal transformers, RAG/Graph-RAG, LLM fine-tuning and LLMOps, inference microservices, responsible AI, and continuous model improvement to drive enterprise-scale outcomes.

Lead design, build, and operate scalable observability systems for distributed cloud and on-vehicle environments. Contribute code and reviews, create technical designs, build tooling to collect observability signals, run PoCs for new tools, partner with engineering teams, define SLIs/SLOs, guide roadmap decisions, and mentor engineers to improve developer productivity and reliability.

Designs, develops, reviews, and troubleshoots secure production software. Delivers Python-based solutions on AWS and Snowflake, drives automation and operational stability, leads engineering best practices, uses AI-assisted development tools responsibly, and contributes to product vision and team culture.

Build and ship production-grade agentic AI workflows across Acquia’s digital experience platform. Architect stateful multi-agent systems using LangGraph, Temporal, and Pydantic; own LangFuse observability, evaluation, and prompt management; establish standards for RAG and tool calling; partner on secure, reliable enterprise architectures; evaluate AI tooling; mentor engineers; and support customer architecture and roadmap discussions.

Support the design, development, and productionization of autonomous AI services and multi-agent workflows. Assist with integrating agent communication protocols (MCP, A2A), hybrid control flows, sandboxed tool execution, and scalable microservice architectures. Collaborate with cross-functional teams under senior guidance to translate business requirements into technical agent specifications and deploy resilient LLM-powered systems.

Take Deepgram's speech models and optimize, compile, and run them on resource-constrained embedded and edge hardware. Work across model compression, runtime performance (C/C++/Rust), RTOS/bare-metal and embedded Linux environments, vendor NPUs/DSPs and inference runtimes, benchmarking, deployment pipelines, OTA updates, and partner with silicon vendors to enable real-time, low-power on-device voice experiences.

Lead design and delivery of scalable AI/ML systems: data wrangling, model training, deployment, and infrastructure. Manage teams, mentor staff, own project planning/budgeting, and collaborate with stakeholders to drive AI-driven business insights.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Architect and scale an internal AI platform and APIs to integrate LLMs into developer workflows. Build autonomous agents, RAG systems, and production-grade tooling to boost developer productivity, ensure reliability, security, and cost-efficiency, and collaborate across product, infrastructure, and security teams.

Design and implement AI agents and Model Context Protocols to integrate enterprise systems (NetSuite, Zuora, Jira) with LLMs. Build secure, SOX-compliant automation and orchestration workflows, measure KPIs, and partner with Finance, IT, and Security to operationalize AI across enterprise applications.

The AI/ML Engineer will design, build, and deploy machine learning systems, optimize workloads, and ensure scalability and compliance of the ITMS platform.

The Forward Deployed AI Engineer develops AI solutions using LLMs, collaborates with customers on their AI strategy, and executes high-stakes projects to solve real-world problems.

Lead design and deliver SAP BTP AI solutions for Finance. Partner with SAP Finance teams to identify AI use cases, build GenAI capabilities (RAG, Joule Skills, agents), ensure secure, scalable integrations, validate feasibility via POCs, and produce production-ready solutions for reconciliations, controls, reporting, and financial close.

Lead design and delivery of AI-assisted engineering across enterprise projects: build production services, direct and review AI-generated code, implement agent workflows, improve testing/observability, and establish repeatable, secure AI-first delivery practices.

Lead development of production AI-assisted engineering systems: build services, APIs, integrations, and tooling; direct and review AI coding agents and AI-generated code; establish repeatable practices, observability, testing, and secure delivery for enterprise platforms.

The role involves developing and deploying AI-powered products, optimizing performance, and collaborating with cross-functional teams to advance AI technology at Capital One.

As a Lead AI Engineer, you will develop and support AI products, enhance system performance, and collaborate across teams to innovate AI solutions for Capital One.

Lead AI Engineer responsible for developing AI-powered products, optimizing large language model inference, and collaborating across teams to enhance customer interactions and business value.

Lead design, build, deploy, and support foundation model training and LLM inference systems, similarity search, guardrails, evaluation, and observability. Optimize production AI for scalability, cost, latency, and throughput. Collaborate cross-functionally, mentor engineers, and shape long-term foundational AI technical vision and roadmap.

Design, build, and operate production-grade AI tools, automations, and integrations for HR workflows. Lead LLM-based solutions (prompting, RAG), integrate people systems (Workday, Slack, Google Suite), improve data quality, support end users, and drive measurable productivity and product roadmap decisions.

Lead AI Engineer responsible for designing and developing AI-powered products, utilizing ML algorithms, and optimizing large-scale AI systems. Collaborate with cross-functional teams to enhance customer experience and drive AI initiatives at Capital One.

Lead and mentor a team to design, build, and operate agentic LLM systems and production LLM-backed APIs. Own end-to-end ML lifecycle, MLOps, inference optimization, vector memory, safety/guardrails, and cross-functional governance for scalable, secure AI deployments.

Lead and guide agile teams to design, build, and deliver secure, scalable AI-enabled software. Develop and review production code, drive product and technical decisions, champion enterprise AI-assisted development practices, implement RAG/LLM architectures and vector stores, integrate cloud services and CI/CD, and establish responsible AI guardrails and validation standards.

Lead architecture and hands-on delivery of production-grade LLM and agentic AI platforms: design cloud-native AWS services, retrieval and RAG, observability, evaluation frameworks, secure APIs, performance/cost optimization, and mentor senior engineers while defining engineering standards for reliable, safe AI operations.