Top AI Engineer Jobs

Leads the design, prototyping, development, deployment, and governance of generative and agentic AI solutions. Builds and fine-tunes machine learning models, manages data pipelines, develops scalable Python services, and applies MLOps practices in containerized environments. Researches emerging AI technologies, leads proof-of-concept initiatives, and collaborates with engineers, architects, product managers, and business stakeholders to deliver production-ready AI systems.

Designs, develops, deploys, evaluates, secures, and maintains enterprise AI, machine learning, generative AI, and LLM solutions for government applications. Responsibilities include data preparation, model training and monitoring, evaluation, red-team testing, safety controls, documentation, governance, privacy protection, cybersecurity compliance, architecture reviews, and stakeholder collaboration. The role translates mission and technical requirements into production-ready, responsible AI capabilities.

Build backend services and AI systems for legal drafting, review, and search workflows. Model legal knowledge into ontologies, develop retrieval and evaluation infrastructure, test LLM prompts, optimize queries and performance, and deploy solutions with customers. The role also includes occasional frontend work in TypeScript and React, customer collaboration, and end-to-end ownership of product features.

Lead AI/ML security architecture and engineering initiatives, clarify security requirements across multiple networks, assess and triage threats, manage disruptive-event response resources, and recommend risk-based business modifications. Build enterprise security solutions through hands-on coding, threat modeling, vulnerability assessment, penetration testing, cloud-native development, and security integration into CI/CD workflows.

Lead software engineering projects and developers building distributed microservices and cloud-based systems for personalized marketing and messaging experiences. Collaborate with product managers, create scalable solutions supporting regulatory needs, mentor engineers, evaluate code, and apply modern technologies including Java, AWS, databases, containers, and AI tools.

Leads software engineering projects and developers building scalable, cloud-based backend and full-stack systems. Designs distributed microservices and omnichannel messaging platforms, collaborates with product managers, supports regulatory requirements, mentors engineers, evaluates code, and applies modern programming languages, databases, container orchestration, cloud services, Agile practices, and interactive AI tools.

Develop and deploy AI, machine learning, and data science solutions that strengthen enterprise cybersecurity. Responsibilities include collecting, cleaning, processing, and analyzing cybersecurity data; building models within CI/CD pipelines; collaborating with senior engineers, customers, and source data teams; and applying responsible AI practices. The role also requires ongoing learning across big data, ML, and AI technologies.

Lead design, code, and architect scalable hybrid-cloud solutions for transactional and analytics workloads. Implement and review high-quality code, CI/CD pipelines, and AI/GenAI systems. Collaborate with development leads and stakeholders, present technical updates, prioritize work, and mentor team members to ensure robust, production-grade delivery and monitoring.

Develop physics-informed AI surrogates and simulation-accelerating models for aerospace mechanical systems. Create scripted FEA/CFD datasets, integrate surrogates into engineering workflows, collaborate on R&D proposals, and advance NVIDIA physics‑accelerated AI tools for design and analysis.

Scale and secure the enterprise Databricks platform on AWS for production analytics, ML, and AI agent workloads. Administer workspaces, Unity Catalog governance, CI/CD pipelines, Terraform automation, MCP server integration for agent frameworks, cluster policy enforcement, cost attribution, monitoring, incident response, and mentor junior engineers.

Design, develop, deploy, and support scalable AI applications, automation solutions, digital agents, and generative AI platforms. Lead agentic workflow development using AWS, OpenAI, Bedrock, SageMaker, LangChain, Kubernetes, and Docker. Support the full AI lifecycle, platform reliability, monitoring, security, governance, and continuous improvement. Lead technical workstreams, collaborate with business and technology teams, and mentor less experienced engineers.

Leads the design and development of enterprise cloud, data, and agentic AI solutions using AWS, Amazon Bedrock, LLMs, RAG, and orchestration frameworks. Builds intelligent search, knowledge retrieval, data pipelines, APIs, automation, and infrastructure-as-code capabilities. Establishes architecture and technical standards, ensures security and scalability, collaborates across business and engineering teams, and mentors engineers.

Design, optimize, and deploy machine learning models for mobile, embedded, and edge devices. Responsibilities include model compression, quantization, hardware-specific performance tuning, cross-platform inference runtimes, secure model execution, hybrid edge-cloud architectures, telemetry, benchmarking, and on-device updates. The engineer collaborates with hardware, firmware, and product teams while addressing privacy, security, bias, documentation, and emerging edge AI technologies.

Architect and implement enterprise-scale AI systems for financial and alternative data, including multimodal ML pipelines, NLP, LLMs, computer vision, deep learning, and conversational AI. Lead MLOps, model deployment, observability, RAG, semantic search, and continuous learning initiatives. Collaborate with product and research teams, establish engineering standards, mentor engineers, and advance AI-driven financial analytics solutions.

Design, develop, and maintain enterprise AI applications, including conversational assistants, copilots, autonomous agents, RAG solutions, and workflow automation. Build scalable APIs and microservices integrating foundation models and enterprise systems. Apply prompt and context engineering, memory management, observability, governance, and performance optimization. Collaborate with cross-functional teams to identify automation opportunities and participate in Agile development, code reviews, CI/CD, and DevSecOps.

Build Fountain Forward’s agent-native AI product from prototype to production. Design multi-agent orchestration, tool-use interfaces, RAG systems, Databricks data models, ETL pipelines, and machine learning services. Deploy and monitor models using MLOps practices, MLflow, and CI/CD. Partner with product, data, and client-facing teams to translate business needs into reliable AI capabilities while maintaining clean code, documentation, testing, data quality, and explainability.

Build and deploy production-grade AI applications using Python and Azure. Develop LLM, RAG, and agentic workflows; integrate enterprise platforms, APIs, databases, and Microsoft 365; and create scalable backend services. Partner with stakeholders to translate ambiguous business needs into production solutions while applying strong testing, security, observability, scalability, and performance practices.

Design, develop, and apply AI and machine learning models for mission-critical defense applications. Build multimodal sensor, computer vision, NLP, and large language model solutions using Python and ML frameworks. Analyze large datasets, implement computational models and algorithms, collaborate with software engineers in agile teams, transition research into production-ready software, support proposals and technical reports, and present work to customers and professional audiences.

Architects and delivers production AI systems, owns the lifecycle of models and agents, establishes validation, orchestration, safety, and monitoring practices, and shapes build-versus-buy strategy. Partners with data engineering on Azure Databricks modernization, leads external AI delivery, and develops a growing AI team and operating model. Evaluates emerging technologies, unifies operational data for AI applications, and communicates AI investment value to executives.

Build and deploy production-grade AI, ML, and intelligent automation solutions for Finance use cases. Design scalable systems, data pipelines, automation workflows, web apps, and intelligent agents; collaborate with Finance, Product, Engineering, and IT to deliver, monitor, and scale solutions that improve efficiency and controls.

Design, build, and operationalize production AI/ML systems on Google Cloud for federal defense programs. Lead Vertex AI, MLOps, data engineering, GenAI, security, and compliance initiatives; develop scalable pipelines and deployment platforms; implement monitoring, retraining, evaluation, and secure-by-design controls. Partner with mission stakeholders, mentor engineering teams, lead technical reviews, and document architectures, controls, and operating procedures.

Lead design, build, deploy and optimise enterprise AI/ML and generative AI solutions (LLMs, RAG, agents) for production in a regulated financial environment. Drive MLOps, monitoring, governance, and platform integration across cloud-native services while collaborating with product, data, security, and business stakeholders to deliver measurable outcomes and scale AI capability.

Designs secure, scalable cloud-enabled architectures for AI-assisted modernization of legacy Marine Corps systems. Advises customers and technical teams, recommends AI/ML, data, cloud, and DevSecOps patterns, manages technical risks, and builds hybrid deterministic-agentic pipelines using LangChain and LlamaIndex. The role also fine-tunes large language models, integrates code-as-data techniques such as ASTs and control/data flow graphs, and develops production AI systems using Java, Python, APIs, and Databricks.

Leads teams designing and deploying AI-driven enterprise and cloud security solutions. Responsibilities include developing machine learning systems, integrating data infrastructure and pipelines, performing advanced modeling and analysis, and building AI applications with Python, Java, and C++. The role involves client engagement, strategic problem-solving, stakeholder validation, coaching, process innovation, and operational excellence while addressing complex cybersecurity and privacy challenges.

Leads the design and development of AI-driven enterprise and cloud security solutions. Manages teams delivering data, analytics, and machine learning engineering projects; oversees AI model implementation, data infrastructure, pipelines, and platform deployments. Uses Python and C++ for algorithm development, analyzes data, improves data quality, ensures technical compliance, mentors staff, manages client expectations, and drives innovation across cybersecurity and privacy initiatives.