Top AI Engineer Jobs

Build and operate enterprise-scale AI solutions, including multi-agent systems, LLM architectures, RAG pipelines, distributed training, online inference, and low-latency serving. The role uses Vertex AI and Google Cloud services to create secure, compliant, observable, and production-grade AI workflows. Responsibilities include agent orchestration, governance, monitoring, automation, stakeholder collaboration, and ownership of the complete AI development lifecycle from data collection through deployment and performance monitoring.

Design, build, and deploy AI/ML and generative AI solutions for internal teams and biopharma clients. Integrate AI APIs, create prototypes and production applications, develop data pipelines, and deploy scalable cloud-based systems. Build client-facing tools, dashboards, and intelligent services while evaluating emerging AI technologies. Collaborate with cross-functional teams, provide technical guidance, gather requirements, support platform rollouts, and promote responsible AI, LLM, RAG, prompt engineering, and agent best practices.

Leads assembly, integration, and test architecture for satellite payloads and constellation systems. Develops verification strategies, production test flows, acceptance campaigns, throughput models, and launch processing concepts. Drives design-for-test, automation, risk mitigation, and AI&T metrics across spacecraft subsystems. Authors proposal and customer materials, serves as technical authority, and transitions mature architectures to program teams. Requires extensive hands-on spacecraft AI&T and launch or deployment experience.

Build production AI-powered software for hiring, finance, capital planning, contract analysis, and operational workflows. Develop LLM features, MCP servers, and agentic systems; embed with finance, legal, recruiting, accounting, and operations teams; translate expert workflows into structured data and automated processes; and ship autonomous, user-friendly products rapidly.

Lead architecture and implementation of Together AI’s global GPU cloud infrastructure. Own GPU and network virtualization, in-data-center IaaS, distributed scheduling and management planes, fault-tolerant automation, and customer-facing production systems. Set technical direction across teams, mentor engineers, establish engineering standards, and build scalable services spanning Kubernetes, compute, storage, networking, and GPU infrastructure.

Build AI-powered software automating hiring, finance, capital planning, contract analysis, and operational workflows. Develop production features using LLM APIs, MCP servers, and agentic frameworks; integrate enterprise systems; transform expert processes into structured data and generated work; and collaborate directly with finance, legal, recruiting, accounting, and delivery teams. The role emphasizes autonomy, rapid execution, product judgment, and forward deployment with business experts.

Productionize computer vision and 3D reconstruction systems for embodied AI. Own spatial output quality, robustness, evaluation infrastructure, performance, throughput, cost, and reliability. Diagnose real-world failures involving calibration, scale, coordinate frames, and scene complexity. Collaborate with research, product, engineering, and customers to deliver production-ready capabilities, workflows, and interfaces for simulation, training, evaluation, and deployment.

Lead and mentor an AI engineering team to design, architect, and deploy production-ready multi-agent, LLM-powered systems. Define technical strategy, set architecture and security standards, implement hybrid control flows/state machines, integrate agent-to-tool and agent-to-agent protocols, and collaborate with product and business teams to translate requirements into scalable microservices.

Develop machine learning, statistical, graph-based, and data-mining algorithms for national-security missions. Build, optimize, and validate AI/ML and large language models; create feature vectors, experiments, simulations, visualizations, and predictive analytics; and implement prototypes in production frameworks. Collaborate with subject-matter experts to automate analysis, guide analytic development, and deliver data-driven insights. Experience with LLMs, RAG, agents, GPU computing, and active TS/SCI clearance with polygraph is required.

Designs, builds, and operates cloud-native infrastructure for AI and GenAI workloads across Azure and AWS. Responsibilities include GPU and Kubernetes platforms, networking, storage, model serving, RAG infrastructure, observability, CI/CD, infrastructure as code, security, compliance, reliability, and FinOps. The role defines AI infrastructure architecture and standards, leads solution design, operates platforms using SRE practices, creates reusable platform components, and mentors engineers.

Leads architecture, development, deployment, scalability, reliability, and operational excellence for enterprise AI and machine learning platforms. Transforms AI research prototypes into production solutions involving LLMs, agentic systems, responsible AI, and advanced inference. Establishes MLOps, observability, governance, incident response, and reliability practices while mentoring engineers and serving as a senior technical escalation point for complex production challenges.

Build and deploy reliable AI agents that automate client workflows and integrate with enterprise systems. Responsibilities include mapping processes, designing automation roadmaps, developing evaluation frameworks and regression tests, monitoring agent performance, instrumenting audit trails, scaling systems to production, and leading workforce adoption and change management. The role also codifies reusable patterns across engagements and requires customer-facing collaboration, production AI experience, Python or TypeScript development, Secret clearance, and up to 10% travel.

Design and build resilient data pipelines integrating files, APIs, services, databases, and mission sources. Develop extraction, normalization, enrichment, validation, cataloging, routing, ingestion, and event-driven workflows. Preserve geospatial metadata, provenance, lineage, security attributes, and data quality. Apply AI, RAG, MCP, and agentic capabilities to improve data discovery and access. Build Node.js applications and Oracle Database integrations while collaborating with engineering, cloud, security, AI, and mission teams.

Design, develop, test, evaluate, and deploy AI/ML models across generative AI, classification, prediction, computer vision, NLP, and reinforcement learning. Provide AI/ML engineering support using algorithms, software development, and cloud computing. The role requires five years of AI/ML development, cloud-based deployment, and Python or R experience, plus U.S. citizenship and an active TS/SCI clearance with polygraph.

Manage a portfolio of B2B SaaS accounts through activation, adoption, and renewal support. Guide product and engineering leaders in using Productboard Spark for AI-first product management, configure AI context, coach prompt usage, deliver activation sprints, monitor adoption metrics, and identify expansion signals. Partner with account executives and renewal managers, document customer insights, improve playbooks, and provide product feedback. The role requires customer-facing technical advisory skills, product management knowledge, and professional AI experience.

Build production-grade AI systems and multi-agent workflows that automate B2B trade processes, including collections, credit decisioning, payments operations, and other manual workflows. Develop reliable LLM-powered features with evaluations and guardrails, write clean performant code, collaborate with platform, product, and design teams, and help shape AI product and engineering practices in an early-stage environment.

Design, develop, and deploy generative AI, LLM-powered, and agentic systems for federal clients. Lead engineering best practices, implement secure integrations, enable cloud/DevSecOps pipelines, monitor production performance, and mentor engineers while collaborating across data, cloud, and stakeholder teams.

Develop, research, optimize, and deploy computer vision AI/ML algorithms for remote sensing, including automatic target recognition and multimodal EO/SAR data fusion. Responsibilities include processing large datasets, training supervised and unsupervised deep learning models, deploying solutions to cloud environments, reviewing research, documenting capabilities, and potentially leading junior engineers. The role requires an active TS/SCI clearance and supports defense and commercial applications.

Build and scale AI medical scribe features using LLMs, develop responsive web and iOS experiences, and maintain audio-processing, cloud, and EHR integration infrastructure. Lead projects from concept to production, collaborate with product and design teams, and work directly with customers to gather feedback and rapidly improve the product.

Develop, train, evaluate, deploy, and maintain computer vision and machine learning models for warfighter performance and visual defect or anomaly detection. Translate operational needs into maintainable ML solutions, design validation strategies, optimize model thresholds, and integrate inference into software systems. Collaborate with data engineers, data scientists, architects, subject matter experts, and product owners while communicating technical findings and tradeoffs.

Build and deploy production AI/ML features across the model lifecycle, including GenAI systems, RAG pipelines, data pipelines, evaluation infrastructure, and MLOps. Apply machine learning to manufacturing, supply chain, computer vision, predictive maintenance, forecasting, inventory, and logistics problems. Collaborate with engineering, manufacturing, and operations teams to deliver measurable outcomes.

Design and develop scalable AI platforms and developer tools using Python, MLOps, LLM prompting, and agentic workflows. Build ETL and data pipelines, integrate and deploy models on cloud infrastructure, implement monitoring and reliability dashboards, prototype generative AI applications, and establish testing, documentation, security, and ethical AI standards. Collaborate with data, engineering, and product teams to operationalize intelligent applications.

Leads hands-on design, development, and optimization of AI data movement and distributed storage systems. Responsibilities include integrating NVIDIA NIXL and DDN Infinia with GPU inference platforms, optimizing GPU-to-storage I/O using GPUDirect Storage, RDMA, and NVMe-over-Fabrics, developing KV cache and multi-tier storage strategies, benchmarking production systems, resolving performance bottlenecks, influencing distributed inference architecture, and mentoring engineers.

Lead the architecture and development of AI-powered engineering platforms, agentic development environments, inference infrastructure, and automated quality systems. Oversee GPU clusters, inference services, evaluation frameworks, testing strategies, and engineering guardrails. Guide technical direction, architecture, code reviews, platform productization, and Loop Engineering practices while mentoring engineers and partnering across software, AI research, platform, quality, and product teams.

Design, build, deploy, and operate secure, scalable AI-enabled products and internal tools. Evaluate models and services, develop prompts and context strategies, establish evaluations, monitoring, safeguards, and reusable architectures, and guide responsible AI adoption. Partner with product and engineering teams to prioritize opportunities, balance value and risk, mentor colleagues, and improve AI systems using production data and stakeholder feedback.