Top AI Engineer Jobs

Designs, deploys, operates, and scales GPU/CPU infrastructure for Top Secret data centers and national security missions. Responsibilities include managing Kubernetes and AI clusters, automating on-premise deployments, supporting GPU-as-a-service platforms, operating databases and distributed storage, improving monitoring and availability, collaborating with AI engineers, and mentoring junior engineers. The role requires senior technical leadership across the full infrastructure lifecycle, from design and deployment through operations and continuous improvement.

Designs, deploys, operates, and scales GPU/CPU infrastructure supporting Starshield national security missions. Responsibilities include managing Top Secret data center environments, GPU-as-a-service platforms, large AI clusters, Kubernetes, operating systems, databases, monitoring, distributed storage, and automation. The role improves service reliability, mentors engineers, collaborates with AI teams, and leads technical excellence across the infrastructure lifecycle.

Design, deploy, operate, and scale GPU/CPU infrastructure for Starshield’s national security missions. Responsibilities include managing Kubernetes and AI clusters, bare-metal and virtualized GPU platforms, databases, monitoring, distributed storage, operating systems, and automation. The role improves service reliability across the full lifecycle, collaborates with AI engineers and customers, mentors junior engineers, and leads technical excellence. It requires Linux, Kubernetes, infrastructure-as-code, scripting, software development, and the ability to obtain a Top Secret clearance.

Build and operate production agentic AI systems embedded within business units. Identify high-value automation opportunities, integrate CRM and business application APIs, design orchestration and evaluation frameworks, deploy monitored workflows, and measure ROI. The role requires close collaboration with GMs, sales, operations, customer success, and field teams while contributing reusable AI engineering patterns to a shared library.

Lead architecture and development of a firm-wide software dependency risk platform on AWS and Java. Own data ingestion, dependency graph modeling, APIs, integrations, technical strategy, and production delivery. Establish agentic AI engineering practices using tools such as Claude, with governance for security, resiliency, auditability, and human validation. Mentor senior engineers, solve complex technical problems, and represent engineering across security, risk, compliance, and platform stakeholders.

Analyze datacenter and agentic AI workloads on next-generation RISC-V CPUs. Characterize workload behavior, identify performance bottlenecks using profiling and simulation, reduce production workloads for modeling, correlate simulations with hardware, and recommend architectural improvements. Collaborate with CPU architects, RTL designers, compiler teams, and software engineers across hardware/software boundaries to improve CPU performance, scalability, throughput, and efficiency.

Design and develop AI SoC hardware through microarchitecture definition, RTL coding, simulation, and logic optimization. Perform timing, power, area, and design-integrity quality checks; integrate and validate IPs; support verification and resolve test failures. Apply secure development practices and drive quality assurance for IP and SoC handoff. Collaborate across multidisciplinary teams to deliver high-quality designs under demanding schedules.

Develop and deploy LLM and NLU systems that analyze clinical notes, medical records, and healthcare standards. Build reliable evaluation, benchmarking, governance, explainability, and data pipeline capabilities while integrating AI features into the platform. The role also involves backend and data engineering, collaboration with backend engineers, maintaining privacy and security standards, and supporting accurate clinical assessments and care recommendations.

Build and ship full-stack features, integrations, and enterprise customizations for an open-source AI product. Work directly with technical partners and enterprise customers on deployments, pilots, troubleshooting, infrastructure, identity, integrations, and data access. Generalize customer needs into reusable product capabilities, maintain customizations, document deployment processes, and provide roadmap feedback. The role requires strong communication, independent execution, and experience supporting AI-enabled applications in enterprise environments.

Build and deliver production backend, platform, and AI capabilities using C#/.NET and Python. Develop distributed systems, LLM applications, AI agents, retrieval workflows, MCP servers, document understanding, and scalable AI infrastructure. Provide hands-on Staff-level technical leadership through architecture, coding, technical direction, mentoring, deployment, reliability, safety controls, and production operations.

Administer and secure AI platforms across Microsoft 365 Copilot, Azure OpenAI, hybrid cloud, and on-premises environments. Manage identity, networking, governance, data flows, compliance controls, Kubernetes workloads, monitoring, quotas, token usage, and AI cost optimization. Provide operational support to developers, maintain documentation and compliance artifacts, and troubleshoot enterprise AI services in a CMMC Level 2 environment.

Develop and modernize microservices-based, real-time air traffic control software on a hybrid cloud platform. Build REST APIs and event-driven integrations, integrate relational and NoSQL databases, optimize distributed services, troubleshoot complex systems, conduct code reviews, document designs, and participate in SAFe/Agile delivery. The role also involves legacy modernization, API versioning, performance engineering, mentoring, and adoption of AI-assisted development practices. Public Trust eligibility and U.S. citizenship are required.

Develop and maintain AI applications and technical solutions alongside engineering teams. Build tools for analyzing large-scale data, debug and test software, optimize performance, and create technical documentation and user support guides. Collaborate with staff and leadership to solve complex customer problems and leverage modern LLM tools to improve development workflows and code quality.

Support the design, development, testing, deployment, and optimization of AI and machine learning solutions for legal technology applications. Work with senior engineers on model evaluation, fine-tuning, retrieval and reasoning improvements, troubleshooting, MLOps, documentation, and production-grade enterprise AI systems.

Lead AI infrastructure projects, develop GenAI solutions, drive automation strategy, and collaborate with IT and business teams for implementation.

Paid part-time summer internship for a high school senior interested in AI prompt engineering and software development. The intern will learn Agile software delivery, use AI tools to support user stories and documentation, test software, identify bugs, participate in sprint activities, and collaborate with developers and stakeholders on secure mission-critical applications. No prior IT work experience or college degree is required.

Build AI-powered finance applications using Databricks, Python, LLMs, and governed financial data. Develop tools for variance commentary, financial close checklists, reconciliation, FP&A, natural-language data exploration, and financial analysis. Partner with finance stakeholders, measure adoption and productivity gains, and maintain governance separating AI recommendations from financial controls. The role requires software engineering, finance process knowledge, and experience with Databricks, LLM applications, finance systems, and production-ready applications.

Owns the analytics layer between curated enterprise data and business users. Builds governed Gold-layer data products, dimensional models, Power BI semantic models, standardized metrics, and reusable data assets. Implements Unity Catalog governance, metadata, lineage, security, quality monitoring, reconciliation, and deployment controls. Partners with analysts, business owners, IT, data stewards, and AI engineers to deliver trusted, documented data for self-service analytics and AI consumption.

Build and operate governed data pipelines using Microsoft Fabric, Azure, Python, PySpark, Spark, and SQL. Implement Medallion Architecture, ingest data from APIs and enterprise systems, and develop lakehouse and Azure SQL data products supporting analytics and AI. Use Git and CI/CD for deployments, explore AI agentic workloads, and partner with architects and stakeholders to translate requirements into scalable solutions. The role requires local travel, occasional overtime and weekends, and in-person work flexibility.

Support an enterprise sales pod by researching target accounts, identifying stakeholders, executing AI-assisted multichannel outbound, booking qualified meetings, and creating pipeline. Build AI agents, workflows, automations, dashboards, and GTM assets that improve research, personalization, prioritization, follow-up, and deal progression. Partner closely with account executives on stakeholder mapping, multi-threading, opportunity advancement, and ARR generation.

Lead GenAI strategy and implementation for government customers, serving as a technical advisor and hands-on builder. Identify high-value use cases, design architectures using the OpenAI API, develop prototypes, guide production deployments, manage customer and partner relationships, and provide feedback to product and research teams. The role also involves scaling deployment engineering practices, publishing technical resources, and collaborating across sales, engineering, research, and policy teams.

Lead the architecture and delivery of end-to-end Azure data platforms, including ingestion, transformation, modeling, analytics, activation, APIs, and agentic AI integrations. Build production-grade batch and streaming pipelines using Microsoft Fabric, Azure Databricks, PySpark, Python, and SQL. Establish governance, security, lineage, and performance standards; troubleshoot critical systems; mentor engineers; advise client stakeholders; contribute to presales and reusable engineering frameworks.

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.

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.