Top AI Engineer Jobs

Lead development of an agentic AI platform: build agent orchestration, RAG/CAG pipelines, embeddings and vector-store ops, integrate LLMs (Anthropic Claude primary), deliver full-stack features across Python/Node/React, own production deployments, evaluation pipelines, and mentor a small senior engineering team while working hybrid in Denver.

Design, develop, and deploy ML and deep-learning solutions (including generative AI). Lead end-to-end model lifecycle, build RAG/agentic workflows, integrate models into production, collaborate with engineering and clients, monitor performance, and document experiments and data provenance.

Leads the architecture, development, deployment, and scaling of production-grade AI agents and intelligent systems. Sets technical direction, establishes AI engineering standards, evaluates emerging technologies, and develops reusable platforms for orchestration, retrieval, evaluation, observability, and guardrails. Partners with business and technology stakeholders to translate ambiguous needs into scalable solutions, mentors engineers, conducts design and code reviews, and guides products from experimentation through production adoption.

Conduct HPC and scientific application benchmarking, optimize performance on HPE systems, analyze bottlenecks across compute, memory, accelerators, interconnects, MPI, and I/O, and provide technical guidance to sales, marketing, development, and customers. The role requires expertise in Linux-based HPC environments, parallel programming, performance analysis, GPU technologies, storage systems, and large-scale application scaling.

Builds, secures, and operates AI development infrastructure and production systems using DevSecOps practices. Responsibilities include creating CI/CD pipelines, Infrastructure as Code, deployment automation, and reusable infrastructure modules; managing Kubernetes and Docker workloads across cloud, on-premises, and classified environments; implementing security, identity, vulnerability, supply-chain, and observability controls; and supporting AI integration across mission environments.

Design and build distributed systems for AI workloads, improve reliability and efficiency, and develop infrastructure automation tools.

Architect and scale high-cardinality observability and telemetry infrastructure for AI cloud platforms. Build Kubernetes exporters, eBPF diagnostics, hardware and network monitoring, dashboards, alerting, and GPU/network utilization billing pipelines. Establish telemetry standards for AI workloads, support proactive remediation, collaborate with GPU and scheduling teams, lead architecture reviews, and mentor engineers. The role requires expertise in Go, Kubernetes, Prometheus/OpenTelemetry, Linux performance tuning, eBPF, AI hardware metrics, and large-scale cloud or HPC telemetry systems.

Design, implement, and maintain CI/CD and MLOps pipelines, provision and scale cloud-native AI infrastructure (Kubernetes, GPU clusters), enforce IaC practices, implement observability and security frameworks, lead incident response and cross-functional platform engineering to ensure high availability and compliance.

Lead the architecture and implementation of AI workload scheduling and orchestration for a large-scale NeoCloud platform. Build batch and gang scheduling, admission control, queueing, topology-aware placement, GPU sharing, and multi-tenancy capabilities using Kubernetes technologies. Optimize NVLink and InfiniBand utilization, integrate scheduling with hardware and storage systems, improve reliability in HPC environments, resolve contention and deadlocks, and mentor engineers while guiding architectural decisions.

Architects and operates high-performance storage infrastructure for AI training and inference. Responsibilities include developing Kubernetes CSI drivers, integrating GPUDirect Storage, optimizing NVMe caching and I/O performance, supporting RDMA and high-speed networking, monitoring storage systems, managing quotas and multi-tenancy, and mentoring engineers.

Build and deploy internal AI tools, agents, integrations, automation, and workflows that improve productivity across engineering, research, operations, and recruiting. Drive adoption through enablement, documentation, training, and best practices. Evaluate AI tools, measure impact, and ensure secure data access and permissions while partnering across technical and business teams.

Lead the architecture and development of AI orchestration platforms, multi-agent workflows, RAG systems, scalable data pipelines, and enterprise AI components. Build MLOps and LLMOps capabilities, responsible AI guardrails, governance, observability, evaluation, and telemetry solutions. Drive architecture reviews and cross-team alignment while enabling secure, auditable AI applications across software engineering functions.

Lead the analytics function by building Power BI and Superset dashboards, conducting deep-dive analyses, and applying AI and NLP tools to generate insights. Own reporting data observability, SQL and database performance, production support, capacity planning, and platform reliability at scale. Collaborate with customers and cross-functional teams on analytics solutions and roadmap development while building and mentoring a high-performing analytics team.

Deploy, integrate, operate, and optimize high-performance NFS storage for GPU-accelerated Kubernetes and AI platforms. Configure Linux storage and networking, integrate CSI storage into k0s and K0rdent clusters, automate provisioning through Terraform/OpenTofu and GitOps, and build observability for capacity, performance, and reliability. Diagnose end-to-end storage issues across hybrid, edge, and air-gapped environments while establishing operational standards.

Serve as the technical sales and customer-facing expert for an AI platform. Responsibilities include conducting product demonstrations, supporting proofs of concept, advising on architecture and development, planning professional services projects, tuning retrieval and agentic pipelines, responding to RFPs, supporting customer adoption, and communicating product feedback internally. The role requires strong AI, cloud, on-premise, application development, storytelling, and enterprise customer engagement experience.

Build and productionize AI-powered backend services for CoCounsel International using Python, FastAPI, PostgreSQL, AWS, and modern LLM providers. Design scalable platform architecture, agentic workflows, orchestration, routing, guardrails, telemetry, and observability. Collaborate with Labs, product, and design teams to turn early AI concepts into reliable customer-facing features, while mentoring engineers and supporting performance optimization and operational robustness.

Designs and operates production multi-agent AI systems, LLM workflows, scoring and optimization algorithms, and closed-loop autonomous processes. Responsibilities include AI model evaluation, production monitoring, governance, third-party AI integration, federated data retrieval, and identifying patentable inventions. The role requires deep machine learning expertise, algorithm development, and deployment experience across complex operational environments.

Own end-to-end features across Go backend services and React/TypeScript frontend applications. Design, develop, and debug high-load distributed systems, APIs, integrations, and event-driven services. Use AI coding agents as part of daily development, critically reviewing and verifying generated output. Contribute to architecture decisions, resolve complex production issues, and improve engineering practices for AI-assisted delivery.

Lead the consolidation of fragmented robotics services into a unified, scalable production platform. Establish architecture, version control, CI/CD, testing, OTA updates, observability, reliability practices, and customer-system integrations. Own production uptime and incident response while mentoring engineers, defining engineering standards, and eventually leading the software team.

Provides AI/ML systems engineering and technical advisory support to a U.S. Government program office. Responsibilities include acquisition planning, source selection, AI/ML technology and architecture evaluation, Agile SAFe program activities, alternatives analysis, lifecycle systems engineering, risk and schedule management, technical assessments, and executive briefings. The role supports space and ground systems, vendor evaluations, research and development, integration, testing, deployment, and coordination across government stakeholders.

Provide SETA systems engineering and acquisition support for AI/ML software and machine learning analytics programs supporting the NRO Ground Enterprise. Responsibilities include AI/ML data analysis, model development and evaluation, software architecture, cloud-system assessments, contractor oversight, technical roadmaps, acquisition documentation, program risk tracking, and technical briefings. The role requires SharePoint development, web technologies, automation tools, agile practices, responsible AI knowledge, and an active TS/SCI clearance with polygraph.

Provides systems engineering and technical assistance to government program offices supporting satellite assembly, integration, testing, launch, and operational readiness. Oversees contractor AI&T activities, technical reviews, requirements verification, risk mitigation, anomaly resolution, mission assurance, and acquisition planning. Coordinates with government leadership and contractors while evaluating technical, cost, schedule, and performance objectives across major program milestones.

Build and operate Sysdig’s AI platform, including MCP servers, agent infrastructure, Bedrock runtime and gateway services, usage and cost pipelines, governance controls, and secure AI automations. The role requires production ownership, AWS expertise, full-stack development, containerized deployments, CI/CD, LLM application development, observability, access control, and strong independent execution.

Leads the design, development, and production integration of Generative AI solutions into distributed software systems. Evaluates AI models and tools, builds robust AI-enabled applications, leads design and code reviews, manages multiple sprint teams, mentors engineers, and partners with product, data science, and engineering teams to deliver scalable, measurable AI capabilities.

Senior Software Engineer focused on AI infrastructure performance insights and observability. Responsible for designing and building monitoring, instrumentation, metrics, and tooling to measure and optimize compute and platform performance while collaborating with infrastructure and ML teams.