Top AI Engineer Jobs

Build and scale AI agents for sales enablement across frontend and backend systems. Lead development with Next.js, React, TypeScript, Python, Postgres, OpenAI, and FastAPI; design robust AI integrations, engage customers, contribute to roadmap planning, and iterate rapidly in a high-ownership startup environment.

Drive technical sales and customer adoption of Microsoft’s cloud and AI platforms through demonstrations, proofs of concept, hackathons, and architecture workshops. Design secure, scalable AI and cloud-native application solutions, resolve technical blockers, and guide production deployments. Build relationships with developers and platform leaders while maintaining expertise in AI Foundry, Azure AI, responsible AI, application modernization, and AI management and security.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Leads AI engineering and advanced analytics initiatives for AML and sanctions clients. Develops scalable data, visualization, and analytics solutions; validates data integrity, security, and compliance; identifies system linkages and optimization opportunities; manages client relationships and project execution; and mentors teams delivering innovative AI solutions.

Lead design and delivery of advanced data and ML solutions, performing exploratory analysis, statistical modeling, and visualization to inform strategy. Manage and coach teams, collaborate with clients to validate outcomes, and improve analytics processes and data quality to drive business decisions.

Leads the design, development, deployment, and optimization of scalable AI and generative AI solutions. Translates business needs into secure, production-ready architectures; applies foundation models, NLP, retrieval, vector search, and agentic workflows; and implements MLOps and LLMOps practices. Manages project workstreams, evaluates solution performance, ensures security and compliance, collaborates with stakeholders, and coaches junior team members.

Lead design and delivery of scalable AI/ML systems: data wrangling, model training, deployment, and infrastructure. Manage teams, mentor staff, own project planning/budgeting, and collaborate with stakeholders to drive AI-driven business insights.

Design, build, and deploy scalable AI/ML systems: data ingestion, model development (Python, TensorFlow), NLP, software integration (C++, Java), data pipeline management, client collaboration, and mentoring junior team members.

Leads fraud and risk-focused data science initiatives, using Python and advanced analytics to generate strategic insights and improve business performance. Manages client engagements, data pipelines, project execution, and teams while mentoring staff and resolving stakeholder issues. Responsibilities include building data architectures, developing visualizations, improving processes, and maintaining professional, technical, and independence standards.

Build and maintain AI-powered automations, software integrations, internal tools, and workflow systems for an immigration law firm. Automate client intake, document generation, filings, case tracking, billing, and communications using Claude and other platforms. Develop custom software incrementally, handle confidential information securely, train attorneys and staff, and document systems for long-term maintainability.

Develop scalable AI services spanning cloud and edge environments. Build APIs, microservices, data pipelines, and agent orchestration runtimes supporting LLM tool use, memory, and multi-step reasoning. Collaborate with researchers and infrastructure teams to deliver AI solutions, optimize real-time inference, and implement security and privacy practices. The role requires backend development experience and proficiency in Python, Java, and C++, with familiarity with databases, streaming, distributed AI, and on-device frameworks preferred.

Develop and optimize machine learning software for embedded edge devices, integrating inference runtimes, AI accelerators, firmware, drivers, sensors, and host applications. Convert models into production-ready implementations optimized for latency, memory, power, and throughput. Responsibilities include quantization, pruning, benchmarking, profiling, debugging, validation, documentation, and cross-functional platform integration across PCs and connected devices.

Lead customer-facing Edge AI solution engagements from discovery and architecture through POCs, production deployment, and scale. Design solutions using accelerated computing, AI inference, computer vision, containers, networking, and hybrid cloud. Own POC execution, productionization planning, technical troubleshooting, customer presentations, architecture documentation, and sales support. Collaborate with sales, engineering, services, partners, and enterprise customers to convert Edge AI opportunities into measurable business outcomes.

Leads the design, implementation, maintenance, and optimization of scalable AI software systems for HP commercial PCs. Oversees operating system and AI application configuration, testing, performance monitoring, troubleshooting, and integration. Gathers infrastructure requirements, leads software systems engineering teams, manages development partners, reviews designs for quality and compliance, introduces new technologies, and mentors less-experienced engineers.

Advises enterprise clients, designs tailored Edge AI and cloud solutions, and supports strategic sales efforts with technical expertise. Responsibilities include solution architecture, scalability planning, product demonstrations, sales enablement content, market research, client feedback, complex problem-solving, and mentoring junior team members. The role partners closely with sales and internal teams to position offerings, address customer challenges, and improve products and sales strategy.

Leads customer-facing Edge AI solution engagements from discovery and architecture through POCs, production deployment, and scale. Designs solutions using accelerated computing, AI inference, computer vision, containers, networking, and hybrid cloud. Owns POC execution, productionization planning, troubleshooting, technical presentations, proposals, and bills of materials. Partners with sales, engineering, services, ISVs, and customers to translate business needs into secure, scalable architectures and measurable outcomes.

Build and maintain large-scale machine learning infrastructure, data pipelines, analytics platforms, and distributed systems in AWS. Develop and scale model training and deployment frameworks, implement ETL and real-time data processing, and create CI/CD and observability solutions. Research reinforcement learning algorithms, including Monte Carlo Tree Search, and validate multi-agent models using simulation platforms. Partner with engineering and business teams, advise management on technical strategy, coordinate complex projects, and mentor junior engineers.

Leads development of full-stack, cloud-native software using Java, Python, AWS, microservices, APIs, and PostgreSQL. Designs scalable and secure systems, builds LLM and generative AI capabilities, and integrates AI agents into engineering workflows. Improves operational stability through automation and observability while contributing to Agile delivery, system architecture, and technical standards. Collaborates across initiatives and promotes responsible, secure validation of AI-assisted development outputs.

Delivers enterprise GenAI training for technical and non-technical employees, develops curricula and multimedia learning materials, supports AI communities, and promotes responsible AI adoption. Integrates and tests autonomous hardware and software systems, translates requirements into specifications, selects sensing and control components, resolves hardware-software interfaces, and conducts verification, validation, and system testing. Collaborates with stakeholders to improve training, autonomous functionality, and AI adoption.

Develops and validates predictive, machine-learning, and deep-learning models; performs exploratory statistical analysis; processes large datasets using GPU, distributed, and cloud computing; creates visualizations and recommendations; and collaborates cross-functionally to improve products and processes. The role requires Python, TensorFlow, PyTorch, large-dataset management, and eligibility for a Secret clearance as a U.S. citizen.

Design and maintain formal ontologies, knowledge graphs, semantic reasoning engines, and data integration pipelines for secure defense and intelligence environments. Develop SPARQL queries, semantic APIs, ontology alignment strategies, governance frameworks, and validation processes. Collaborate with AI/ML engineers and mission stakeholders, support secure production deployments, document semantic architectures, and mentor junior engineers. The role requires expertise in OWL, RDF, RDFS, SPARQL, graph databases, semantic modeling, data integration, and secure data engineering.

Leads responsible AI and autonomy adoption across Lockheed Martin by integrating capabilities into military experimentation, wargames, simulations, demonstrations, and customer engagements. Translates defense mission needs into executable technical concepts, directs multidisciplinary teams, supports AI standards and assurance, and advises senior military and business leaders. The role requires extensive defense technology transition experience, strong executive communication, and an active Top Secret clearance with TS/SCI eligibility.

Leads end-to-end delivery of AI solutions in production, translating customer needs into roadmaps and managing schedules, budgets, resources, and risks. Serves as a technical liaison across multidisciplinary teams, overseeing prototyping through deployment and support. Builds data pipelines, analytics capabilities, dashboards, and KPI tracking to measure efficiency gains. Requires Python, machine learning, project and budget management, stakeholder negotiation, and U.S. citizenship with eligibility for a Secret clearance.

Improves the reliability, scalability, and observability of AI development and deployment platforms. Develops automation, CI/CD and GitOps workflows, dashboards, alerts, tests, and release validation processes. Investigates incidents, reduces operational toil, supports Kubernetes-based infrastructure, and collaborates across engineering teams on platform health. The role also includes autonomous-system integration, verification, validation, testing, and hardware/software interface troubleshooting.

Develops and validates predictive and deep-learning models through exploratory research and statistical analysis. Designs scalable GPU, distributed, and cloud computing solutions for large datasets; creates visualizations and actionable recommendations; and collaborates cross-functionally to improve products and processes. The role uses machine learning, optimization, simulation, and potentially NLP or Generative AI to deliver business value.