Top AI Engineer Jobs

Build and operate enterprise AI infrastructure, including LLM integrations, automation, observability, deployment pipelines, agentic workflows, vector databases, and RAG systems. Troubleshoot complex production issues, optimize reliability, performance, token usage, and costs, and develop scalable self-service tooling. Partner with engineering, product, business systems, and AI strategy teams to improve internal AI capabilities.

Build and maintain AI-powered solutions, automated workflows, scripts, integrations, and data processes using Python, SQL, PowerShell, APIs, and workflow platforms. Apply AI and LLM technologies to document processing, information retrieval, and business automation. Partner with internal departments, banking vendors, and service providers to improve systems, data quality, reporting, security, and compliance. Monitor production automations, evaluate emerging technologies, document procedures, and deliver measurable operational improvements.

Build reusable SDKs, tools, templates, sample applications, and reference implementations that help engineering teams develop, test, deploy, and adopt AI solutions. Create documentation and onboarding resources, support AI use cases such as assistants, summarization, RAG, and agent workflows, and partner with engineering and product teams to improve developer experience, scalability, reliability, and adoption.

Partners with account executives to lead technical sales strategy, customer discovery, product demonstrations, workshops, and proof-of-value evaluations. Translates customer goals into Datadog solutions, develops technical champions, manages evaluation timelines, provides product feedback, documents opportunity progress, and collaborates with Product, Support, and go-to-market teams to resolve technical blockers and drive adoption.

Own end-to-end development of production-grade AI-enabled applications across React and TypeScript frontends, Python and Node.js backends, PostgreSQL data models, cloud infrastructure, and CI/CD. Translate complex insurance compliance requirements into intuitive software, maintain platform reliability, optimize performance and accessibility, validate AI-generated code, and collaborate with product, design, and business stakeholders. Mentor engineers and drive technical planning, documentation, testing, deployment, and continuous optimization.

Develop algorithms and high-performance systems across the GenAI lifecycle, including multimodal data preparation, distributed foundation-model training, reinforcement-learning post-training, inference optimization, and open-source AI infrastructure. The role involves improving scalability, efficiency, deployment cost, and model support while collaborating with research, product, and infrastructure teams.

Build and own evaluation systems for AI products used in banking operations. Design representative tests, scoring criteria, expert-reviewed benchmarks, and automated comparisons across product versions. Investigate failures, verify accuracy and citations, assess safety, fairness, privacy, and human-review controls, and monitor live performance. Document evidence for release decisions, recommend improvements or restrictions, mentor colleagues, and support client proposals, estimates, recruiting, and implementation activities.

Leads AI initiatives and serves as the technical contact for a federal customer while developing production AI/ML solutions in Python. Designs data pipelines, ETL/ELT processes, database schemas, SQL queries, and stored procedures. Builds Azure-based data environments and integrates backend systems with Power Apps and Power Automate. Translates stakeholder requirements into scalable AI and data solutions, with potential work involving generative AI, RAG, and LLM orchestration.

Leads the design and delivery of knowledge graph, semantic, and AI solutions for enterprise clients. The role includes ontology and schema design, graph-based machine learning, AI data pipelines, data architecture, client consulting, business-case development, thought leadership, and team or workstream leadership. Candidates will work across technical, research, product, and client teams to develop scalable solutions using graph, vector, cloud, NLP, and LLM technologies.

Deploy and benchmark LLM inference on NVIDIA GPU systems, configure KV cache offloading to network-attached storage, analyze performance bottlenecks with Python, and collaborate with FPGA, SoC, and firmware engineers to validate end-to-end data paths. The intern will quantify storage requirements, maintain reproducible experiments, and present findings that inform future SSD controller architectures.

Build and deploy production AI integrations, automations, and internal tools using LLM APIs, agent frameworks, and cloud platforms. Lead client discovery, AI strategy, migrations, workflow integrations, proofs of concept, and solution delivery. Monitor and evaluate AI applications, improve quality and performance, and apply security, privacy, and governance practices. Collaborate in pre-sales, scope projects, estimate effort, and communicate technical solutions to non-technical stakeholders.

Architect and build scalable, secure cloud infrastructure for SaaS platforms. Lead CI/CD automation, Kubernetes-based microservices scaling, infrastructure-as-code adoption, cross-functional DevOps projects, and AI-assisted operational improvements. Collaborate with engineering, QA, and product teams to improve delivery, reliability, cost efficiency, and developer productivity. Participate in on-call rotation and serve as a DevOps subject matter expert.

Build automation for NVIDIA switches and InfiniBand fabrics, including configuration management, drift reconciliation, NAT gateways, L4 load balancing, and resilient network control loops. Define large-scale network behavior around BGP, convergence, failover, and production failures. Lead technical direction, design standards, and customer deployments while writing production software in Go or Python for AI infrastructure.

Implements and monitors cybersecurity controls, assesses vulnerabilities, responds to incidents, supports remediation, and helps maintain security policies and incident response plans. The role conducts tabletop exercises, business continuity and disaster recovery testing, collaborates with IT operations and service providers, supports the service desk, and educates staff on security practices. Regular travel to remote plant and farm facilities, physical work, and occasional after-hours support are required.

Lead the enterprise AI security and Agentic AI program by designing secure AI agents for cybersecurity operations, integrating Microsoft AI and security platforms, and establishing governance, risk controls, auditability, and responsible AI practices. Build reusable frameworks and guardrails addressing prompt injection, permissions, data leakage, and unauthorized actions. Partner with Security, Risk, Compliance, Legal, and Technology teams to enable compliant enterprise AI adoption.

Develop an end-to-end evidence management and synthesis system using data pipelines, publication search and deduplication, machine learning, and LLM workflows. The intern will integrate AI tools, manage data, collaborate with cross-functional stakeholders, and present findings. Required skills include Python, SQL, machine learning concepts, systems design, communication, and current enrollment in a relevant bachelor's or master's degree program.

Build and evaluate secure machine learning systems, investigate AI vulnerabilities and adversarial attacks, develop defensive techniques, prototype emerging technologies, and transition AI capabilities to government sponsors. Collaborate with researchers and technical teams, communicate complex concepts, mentor junior staff, and support applications including computer vision, NLP, planning, robotics, and trustworthy AI.

Design and build multi-agent applications using Google's ADK, including agents, tools, backend APIs, databases, and TypeScript web interfaces. Deploy and operate services on GCP with Cloud Run and Agent Engine. Develop evaluation suites, feedback loops, tracing, logging, authentication, CI/CD, and comprehensive testing to ensure reliable agent behavior. Integrate Gemini models, REST and MCP tools, and customer systems while using AI coding assistants and collaborating with technical leads and stakeholders.

Design, configure, and support AI agents and agentic applications within Oracle Fusion Cloud. Translate business requirements into AI-enabled solutions, develop workflow agents and automations, configure integrations, and troubleshoot security, data, performance, and production issues. Support testing, deployment, documentation, governance, and compliance while evaluating new Oracle AI capabilities. Collaborate with stakeholders and mentor team members.

Leads the architecture, development, deployment, and maintenance of enterprise AI-enabled systems. Designs agentic AI solutions, evaluation frameworks, reusable patterns, and secure integrations while improving model quality through prompting and tuning. Partners with business and technical stakeholders on requirements, architecture, strategy, testing, reliability, monitoring, and production support. Drives AI adoption, mentors engineers, conducts design and code reviews, manages change processes, and guides technology direction across major platforms.

Build customer-facing AI features and platform improvements across the full stack (TypeScript/Node/NestJS/React/Postgres). Develop agentic workflows, CI, and observability; improve reliability, migrations, and developer productivity. Mentor engineers, influence technical direction, and ship high-impact product experiments quickly for studio customers.

Design, deploy, and optimize LLM-powered tools for software development, including RAG pipelines, agentic workflows, coding assistants, automated testing, and documentation. Integrate AI into IDEs and DevSecOps processes while ensuring security, reliability, traceability, and software assurance in aerospace and defense environments. Evaluate models, develop guardrails, support secure and air-gapped deployments, collaborate across engineering and cybersecurity teams, and mentor others.

Design and prototype AI-powered solutions that transform business processes. Partner with business and technology leaders to identify automation opportunities, translate requirements, develop proof-of-concepts and minimum viable products, and define performance metrics. Serve as a technical liaison, document solutions, train users, support adoption, and mentor future AI practitioners while applying LLMs, AI agents, machine learning, NLP, and data modeling.

Design, develop, and deploy enterprise AI applications and agentic solutions using LLMs, RAG, intelligent automation, and machine learning. Build data pipelines, APIs, microservices, and reusable AI components with Python and SQL. Collaborate with architects, data scientists, product owners, and engineering teams in a regulated financial-services environment. Lead multiple AI use cases, oversee AI engineers within a Center of Excellence, document solutions, and evaluate emerging AI technologies.

Design, build, and deploy secure end-to-end web applications integrating AI and large language models in a Department of War environment. Develop frontend interfaces, backend APIs, databases, vector search, and Retrieval-Augmented Generation workflows. Containerize applications with Docker or Podman and support deployment in restricted or air-gapped environments. The role may also involve local open-weight model deployment, runtime optimization, AI orchestration, guardrails, and evaluation.