Top AI Engineer Jobs

Lead the architecture, development, deployment, and scaling of AI-native solutions for federal clients. Build full-stack applications, RAG systems, semantic search, agentic workflows, and reliable data pipelines across structured and unstructured data. Guide embedded engineering teams, integrate solutions into legacy and classified environments, evaluate model performance, uphold responsible AI practices, and serve as the technical lead in client engagements. Travel may reach 100% based on mission needs.

Design, develop, and deploy agentic AI solutions using existing large language models. Build multi-agent workflows, engineer prompts, integrate AI capabilities through APIs and microservices, and evaluate output reliability. Collaborate with system engineers, software developers, and mission experts to automate intelligence reporting and dissemination workflows. Apply AWS, Python, orchestration frameworks, RAG, and MLOps practices while maintaining production-quality AI capabilities.

Build and operate governed, production-grade AI and LLM systems across Velsera’s products and business functions. The role covers multi-cloud model serving, MLOps and LLMOps, evaluation, governance, security, auditability, RAG, agentic workflows, and enterprise integrations. This senior individual contributor will establish reusable AI platform patterns, deliver customer-facing and internal capabilities, support regulated environments, and guide build-versus-buy decisions while collaborating with engineering, product, security, compliance, scientific, and commercial teams.

Design, develop, and deploy enterprise AI applications using LLMs, RAG, AI agents, vector search, document processing, and cloud-native technologies. Build scalable APIs, integrations, knowledge platforms, and Kubernetes-based services while contributing to architecture, CI/CD, security, testing, and observability. Collaborate with business and technical stakeholders to solve complex problems, evaluate emerging AI technologies, and mentor team members.

Designs, optimizes, and deploys machine learning models for resource-constrained edge devices, including mobile and embedded platforms. Responsibilities include model compression, quantization, pruning, hardware-aware optimization, performance profiling, production deployment, and addressing privacy, security, memory, energy, and connectivity constraints. The role collaborates cross-functionally to deliver reliable on-device AI capabilities.

Design and implement multi-agent AI systems for marketing compliance workflows. Build production ML pipelines involving model fine-tuning, prompt engineering, retrieval-augmented generation, evaluation, monitoring, explainability, and feedback-driven personalization. Translate compliance rules into agent logic, ensure auditability across customer deployments, and collaborate with engineering, product, implementation, and compliance teams to deliver reliable AI features.

Lead the full development lifecycle, building scalable APIs and systems, mentoring engineers, and delivering impactful AI-driven software solutions.

Build and deploy production LLM agent systems that automate social commerce, marketing, creator, and ecommerce workflows. Responsibilities include discovery, architecture, development, integrations with advertising and commerce APIs, data pipelines, evaluation harnesses, cloud infrastructure, observability, adoption, and ongoing maintenance. The role requires end-to-end ownership, strong software engineering skills, experience with AI agents and third-party APIs, and understanding of performance marketing metrics and creator economics.

Build and deploy autonomous AI agents and LLM-powered applications for insurance brokerage workflows. Responsibilities include owning the development lifecycle, creating evaluation frameworks and benchmarks, monitoring production performance, optimizing and scaling systems, mentoring teammates, and promoting AI adoption across the company. Requires production software engineering experience, LLM application expertise, dataset development, benchmark creation, and TypeScript proficiency.

Design, build, and deploy scalable ML training and evaluation platforms for autonomous driving. Lead architecture and implementation of distributed, high-performance pipelines, drive cross-team prioritization, mentor engineers, and support recruiting and code quality to accelerate ML model development lifecycle.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

Build and deploy production AI agents for real customers. Responsibilities include orchestrating LLM pipelines, integrating APIs and tools, developing React interfaces, implementing RAG and document-processing systems, deploying serverless applications, and monitoring performance. The role requires strong Python or JavaScript skills, hands-on OpenAI and multimodal LLM experience, vector databases, agent frameworks, function calling, and full-stack integration. Applicants must be pursuing a Master's or PhD in a technical field.

Build the core AI intelligence layer for an energy infrastructure platform. Own technical strategy, architecture, production engineering, agentic workflows, data and geospatial pipelines, security, and observability. Translate energy procurement and project finance workflows into software, automate contracting and diligence, and iterate directly with users to achieve product-market fit. This founding role requires autonomous execution, rapid prototyping, technical leadership, mentorship, and comfort with startup ambiguity.

Build and improve production generative AI systems that create editable motion from natural language. Responsibilities include model orchestration, structured generation, validation, compiler-backed quality gates, fine-tuning, post-training, synthetic data, evaluation, dataset governance, regression testing, and production reliability. The role requires hands-on ownership across LLM behavior, data pipelines, infrastructure, inference optimization, and product quality, with code generation, compilers, multimodal systems, or animation experience being valuable.

Design, train, test, deploy, and maintain AI/ML models (LLMs, RAG, agentic workflows). Build AI-enabled applications and secure data foundations on cloud (AWS). Implement MLOps/DevSecOps, event-driven integrations, and collaborate with engineers, architects, and product owners to deliver mission-focused Air Force solutions. Secret clearance required.

Leads an Agile Release Train delivering an agentic AI web application across product, engineering, AI/ML, architecture, security, QA, DevOps, and operations teams. Facilitates PI Planning, dependency and risk management, release readiness, production deployment, metrics, and continuous improvement. Coordinates AI experimentation and production delivery while addressing model reliability, safety, latency, cost, integrations, observability, and operational risks. Serves as the execution integrator between technical teams, product leadership, and enterprise stakeholders.

Build and maintain internal agentic AI platforms and developer-facing tools that integrate LLMs with engineering workflows. Partner with product and infra teams to design standards for agent deployment, reliability, evals, security, onboarding, and adoption measurement to improve developer productivity.

Lead design and delivery of full‑stack, cloud‑native AI and GenAI solutions; define AI strategies; architect secure, scalable platforms; mentor engineering teams; run pilots, integrate successful technologies, and partner across cloud, security, data, and business stakeholders to improve developer experience and drive enterprise adoption.

Designs, develops, and supports secure AI-powered applications, agents, APIs, integrations, and workflow automations. Builds Generative AI solutions using LLMs, prompt engineering, retrieval patterns, and enterprise data. Responsibilities include full software delivery lifecycle participation, AI output evaluation, stakeholder collaboration, secure engineering, governance, monitoring, risk controls, and operational readiness.

Lead end-to-end development of a greenfield enterprise platform for software dependency risk management. Build Java services, APIs, data ingestion pipelines, dependency analysis capabilities, and developer-facing features on AWS. Guide engineers and teams in secure AI-assisted development, code quality, testing, governance, and operational stability. Provide technical direction, influence architecture and product decisions, support production systems, and collaborate with product managers and designers to deliver scalable platform capabilities.

Design, build, productionize, and operate supervised and unsupervised ML models, LLM solutions, risk features, and Databricks data pipelines for financial crime monitoring. Develop AI feedback and accuracy measurement, monitor model drift, bias, and performance, and support enterprise-grade ML applications in production. Collaborate with product, engineering, SRE, data science, compliance, and operations teams using agile processes.

Lead the research, architecture, and production delivery of agentic and generative AI solutions for complex banking operations. Design collaborating agents, reusable services, evaluation frameworks, and distributed machine learning systems. Establish experimentation, monitoring, safety, reliability, and governance practices while partnering with business, product, and engineering teams. Mentor engineers and researchers and drive technical alignment across stakeholders.

Design, deploy, and improve enterprise AI solutions including LLM workflows, RAG pipelines, agents, classifiers, and copilots. Develop prompt strategies, tool-calling patterns, guardrails, evaluation frameworks, and responsible AI controls. Partner with data, security, privacy, compliance, architecture, and QA teams to ensure solutions are safe and governed. Monitor production quality, accuracy, hallucination risk, latency, cost, observability, and model drift while documenting limitations and operational requirements.

Designs, deploys, maintains, and troubleshoots HPE NonStop payment infrastructure, secure key managers, Etinet servers, and related subsystems. Uses AIOps and AI-assisted practices for incident triage, anomaly detection, automation, documentation, and reliability improvements. Partners with SRE, observability, and cybersecurity teams to govern AI-enabled monitoring, maintain auditability, execute upgrades, and produce operational knowledge such as runbooks and troubleshooting guides.

Lead the design, development, and operation of an enterprise GenAI serving platform. Build high-throughput LLM inference, gateway, routing, GPU optimization, quantization, distributed serving, and observability capabilities. Establish reliability practices, SLOs, performance benchmarks, cost controls, and secure production standards. Drive adoption of agentic AI-enabled engineering workflows with governance, auditability, and human validation while influencing senior stakeholders and enabling reusable platform components across teams.