Top AI Engineer Jobs

Lead design, architecture, and delivery of full-stack, AI-enabled applications. Provide technical leadership across frontend, backend, APIs, databases, and AI integration. Drive engineering best practices, CI/CD, automated testing, cloud-native (AWS) solutions, and adoption of AI-assisted development while ensuring secure, resilient, scalable systems.

Deploy and support computer vision systems at customer sites, including provisioning Linux edge computers, NVIDIA Jetson devices, cameras, networks, and modems. Conduct site assessments, install and validate equipment, coordinate contractors and customer IT teams, manage inventory and shipping, monitor systems, troubleshoot incidents, and document procedures. The role requires regular U.S. travel, occasional off-hours work, physical equipment handling, and clear customer communication.

Build and support production full-stack applications with accessible interfaces, APIs, databases, workflow automation, and AI capabilities. Develop LLM integrations, RAG pipelines, vector search, and agentic workflows while validating AI-generated code. Apply secure development, accessibility, automated testing, CI/CD, MLOps, adversarial AI testing, monitoring, incident response, and Responsible AI practices. Collaborate in Agile sprints and support federal customer requirements.

Designs, builds, and operates production-grade data, machine learning, and generative AI solutions. Responsibilities include scalable batch and streaming pipelines, lakehouse architecture, data modeling, governance, model deployment, MLOps, CI/CD, infrastructure as code, observability, and agentic software development. The role may support fixed-price client delivery across Azure, AWS, and GCP or internal analytics on Azure Databricks, including data integration and quote-to-cash automation.

Designs, develops, tests, deploys, monitors, and continuously improves machine learning models for complex business and national security missions. Integrates data pipelines, evaluates model performance, ensures data and output quality, and transforms prototypes into scalable production solutions. Requires strong expertise in machine learning algorithms, software architecture, data engineering, model validation, and system integration. The role may lead distributed teams and requires an active TS/SCI clearance with polygraph.

Own and optimize Netpreme’s production LLM inference infrastructure, including vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, Kubernetes deployments, benchmarking, distributed serving, KV caching, batching, routing, and scheduling. The role focuses on improving latency, throughput, scalability, reliability, and efficiency across diverse LLM and multimodal workloads while partnering with hardware, model, and systems engineers.

Designs and implements security controls for AI solutions, including threat modeling, adversarial testing, secure development, identity and access management, data protection, monitoring, and incident response. The role builds AI-enabled cybersecurity automation, validates AI deployments, supports engineering teams with secure implementation patterns, and communicates security risks and recommendations to technical and business stakeholders.

Designs, builds, and deploys secure AI applications for Finance, including knowledge assistance, document processing, financial analysis, and workflow automation. Converts prototypes into maintainable production systems with enterprise integrations, permission-aware retrieval, testing, monitoring, audit controls, and human approval workflows. Partners with Finance and Digital teams to define requirements, architecture, and acceptance criteria, while creating reusable components, documentation, and operational support practices.

Leads application and AI security engineering across architecture, software delivery, cloud platforms, APIs, and agentic AI systems. Builds reusable security controls, automated testing, risk-based quality gates, and secure engineering patterns. Conducts code reviews, threat modeling, authorized security testing, vulnerability triage, incident support, and remediation validation. Partners with distributed engineering teams, mentors technical leaders, and communicates security risks to executives, customers, and assurance stakeholders.

Lead end-to-end production AI development, including agentic workflows, document intelligence, underwriting copilots, conversational AI, RAG systems, fine-tuned models, evaluation frameworks, and shared AI platform components. Own design, deployment, observability, reliability, cost, and security of LLM systems while mentoring engineers and collaborating with product, credit, underwriting, and platform teams in a regulated lending environment.

Build and own full-stack, AI-native products for online learning using TypeScript, Vue.js, Node.js, Azure, retrieval pipelines, agentic workflows, model APIs, and LLM evaluations. Lead features from prototype through production, ensuring scalability, security, observability, cost control, and graceful model failure handling. Mentor engineers, establish AI-assisted development standards, conduct code reviews, and collaborate with product and engineering teams in a remote, async-first environment.

Build and deploy GenAI, ML, RAG, and agentic systems while conducting offensive security research and red teaming against AI applications. Identify prompt injection, data leakage, model theft, adversarial attacks, and other vulnerabilities; develop scanning and fuzzing tools; and implement secure-by-design hardening strategies. The role also involves cross-functional collaboration, technical research and writing, threat intelligence monitoring, community representation, and mentoring engineers.

Leads the architecture and delivery of AI-native, full-stack products using TypeScript, Vue.js, Node.js, PostgreSQL, and Azure. Designs retrieval pipelines, agentic workflows, model integrations, evaluation frameworks, observability, and human-in-the-loop experiences. Sets technical direction, leads cross-functional initiatives, mentors engineers, establishes engineering standards, and ensures AI systems are reliable, secure, scalable, accessible, and cost-effective in production.

Build and deploy production LLM applications, agentic systems, RAG capabilities, and intelligent automation on Azure. Own AI capabilities end to end, including data and embedding pipelines, enterprise integrations, evaluation, observability, security, and operational handoff. Work directly with business and engineering teams in a forward-deployed model, transforming ambiguous use cases into production systems and reusable AI accelerators.

Build and maintain Java and Spring Boot backend services for the Compass open-banking platform. Integrate AWS Bedrock models, Knowledge Bases, and Agents to deliver RAG, summarization, and operator-assistance features. Develop ingestion pipelines for banking artifacts, OpenSearch indexing and vector retrieval, REST APIs, Thymeleaf administration views, and CI/CD workflows. Optimize security, scalability, and performance while collaborating with mappings, AI content, and frontend teams.

Hands-on engineer responsible for securing internally developed applications, APIs, AI-enabled solutions, and integrations. The role performs vulnerability assessments, source-code reviews, architecture assessments, penetration testing, and remediation validation. Responsibilities include strengthening authentication, authorization, API protections, CI/CD security testing, cloud application architectures, logging, and monitoring. The engineer also evaluates LLM applications and AI agents for prompt injection, data disclosure, insecure tool execution, and excessive permissions, while communicating risks and corrective actions to stakeholders.

Design and maintain enterprise AI security and risk frameworks aligned with global regulations and standards. Translate requirements into policy-as-code, security baselines, technical controls, and continuous monitoring logic. Assess AI-specific threats such as prompt injection, data leakage, model extraction, shadow AI, and supply-chain vulnerabilities. Partner with AI/ML, infrastructure, security, Legal, Privacy, and assurance teams to embed governance into architectures, CI/CD workflows, and runtime environments. Own AI policy lifecycle, privacy safeguards, model adoption decisions, and cross-border data requirements.

Leads development of an AI-first, enterprise-scale loan origination and servicing platform using C#, .NET, SQL Server, modern UI frameworks, microservices, and event-driven architecture. Designs complex features, integrates AI capabilities and third-party systems, modernizes legacy applications, supports migration, optimizes performance, implements security and testing, troubleshoots distributed systems, and mentors engineers.

Own Compass’s AI product security roadmap and secure generative and agentic AI systems across models, RAG pipelines, tool invocation, and user-facing outputs. Responsibilities include threat modeling, production guardrails, prompt-injection and jailbreak defenses, retrieval security, AI supply-chain assessment, autonomous-agent controls, detection engineering, secure development tooling, and executive-level risk communication. The role requires production security engineering experience with AI/ML systems, cloud and application security, adversarial ML, red teaming, and strong Python or Go skills.

Owns Compass’s AI product security strategy and roadmap. Responsibilities include threat modeling AI features, securing RAG and vector-store retrieval, building LLM guardrails, mitigating prompt injection and jailbreaks, constraining autonomous agents, assessing AI supply-chain risks, developing abuse detection and telemetry, and automating secure-by-default libraries, CI/CD checks, and evaluation harnesses. The role also advises engineering teams and executives on AI security risks and remediation.

Own the security roadmap for Compass’s generative and agentic AI products. Threat model AI features, RAG pipelines, multi-agent workflows, and automated decisioning; build protections against prompt injection, jailbreaks, unsafe tool use, and data exfiltration; secure retrieval layers and AI supply chains; develop monitoring, detections, CI/CD checks, libraries, and evaluation harnesses; and advise engineering teams and executives on AI risk.

Own Compass’s AI product security strategy and roadmap. Threat model AI features, RAG pipelines, agentic workflows, and automated decisioning; build guardrails against prompt injection, jailbreaks, unsafe outputs, and data exfiltration; secure retrieval and AI supply chains; design autonomy controls; implement monitoring and detections; and automate secure development patterns through libraries, CI/CD checks, and evaluation harnesses. Advise engineering teams and executives on AI security risks and architecture.

Build and operationalize AI/ML threat detection systems for identity, network, device, and session telemetry. Develop real-time streaming pipelines, risk scoring and enforcement systems, MLOps platforms, and security controls for autonomous AI agents. Detect identity compromise, privilege escalation, data exfiltration, prompt injection, agent drift, and unauthorized access. Train, evaluate, deploy, and monitor production ML models while enabling autonomous investigation, containment, and remediation.

Secure AI, machine learning, inference, and developer platforms through threat modeling, architecture and code reviews, secure API and distributed-system design, model and supply-chain security, and security automation. Build controls for authentication, authorization, isolation, rate limiting, monitoring, anomaly detection, CI/CD scanning, and protection against prompt injection, model extraction, adversarial inputs, and data leakage. Partner closely with ML, platform, infrastructure, engineering leadership, and the CISO.

Develop and maintain scalable cloud-native web, mobile, API, and data products; migrate services to public cloud; implement CI/CD and test automation; contribute to Agile teams, troubleshoot production issues, and promote secure, high-quality software practices.