Senior Forward Deployed AI Engineer (GenAI, AWS)

Accountabilities •

Embed directly within client teams to understand their operational workflows, business constraints, and objectives before designing and implementing AI-powered solutions.

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Rebuild and optimize business functions from first principles, collaborating with client stakeholders and forward-deployed leadership to translate complex problems into effective technical solutions.

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Design, develop, test, and deploy production-grade generative AI and agentic systems using Claude, AWS, and other relevant technologies, ensuring reliability, scalability, and operational readiness.

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Own the entire delivery lifecycle, from problem discovery and solution architecture to implementation, evaluation, deployment, and continuous improvement.

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Develop and maintain evaluation frameworks for non-deterministic AI systems, defining meaningful performance metrics and validating system quality against real-world requirements.

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Leverage existing industry blueprints and proven implementations as starting points for new engagements, incorporating lessons learned into reusable tools, frameworks, and delivery practices.

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Deliver solutions across AI workflow activation, agentic software development lifecycle (SDLC) initiatives, and industry-specific implementations, particularly in financial services and healthcare.

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Participate in pre-sales activities, technical discovery, and solution scoping, communicating effectively with senior business and technology stakeholders.

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Contribute to the evolution of enterprise AI delivery methodologies, engineering standards, and reusable platforms that support scalable adoption of generative AI.

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Share technical knowledge and strengthen professional visibility through open-source contributions, technical publications, conference presentations, and collaboration with experienced AI engineers.

Requirements:

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Typically 7+ years of professional software engineering experience, including substantial hands-on responsibility for production systems. A strong commitment to remaining actively involved in coding and technical delivery is essential.

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Proven experience deploying generative AI or large language model (LLM) applications into production, with a practical understanding of the challenges that arise beyond prototyping.

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Demonstrated ability to evaluate non-deterministic AI systems by building or owning evaluation suites, defining appropriate metrics, and explaining the rationale behind evaluation methods.

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Strong software engineering fundamentals and the ability to become productive quickly in unfamiliar codebases, programming languages, and technical environments.

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Proficiency in Python and/or TypeScript, with a focus on engineering depth, maintainability, and production-quality implementation.

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Experience designing and delivering cloud-native solutions on AWS, including operational considerations, reliability, scalability, and maintainability. Experience with GCP or Azure is an advantage.

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Hands-on experience with Claude, Claude Code, and Cowork, including skills, workflows, Model Context Protocol (MCP), integrations, connectors, and custom harnesses.

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Ability to understand unfamiliar business functions quickly, engage with end users and operators, and translate real operational needs into effective technical solutions.

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Experience in pre-sales, technical consulting, or solution discovery, with the ability to communicate credibly with senior business leaders, CTOs, and other executive stakeholders.

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Strong ownership, adaptability, and problem-solving skills, with the confidence to navigate ambiguity, define approaches independently, and drive engagements from discovery through production.

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Excellent communication and collaboration skills, particularly in environments where engineers work closely with clients and must balance technical quality with measurable business outcomes.

Nice to have:

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Experience with containerization and orchestration technologies such as Docker, Kubernetes, or Amazon ECS.

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Practical knowledge of CI/CD pipelines and production deployment automation.

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Infrastructure-as-code experience using AWS CDK, CloudFormation, or Terraform.

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Open-source contributions, technical publications, or public writing focused on applied AI and software engineering.

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Experience working with regulated industries, particularly financial services, insurance, or healthcare.

Benefits:

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Competitive compensation: Base salary of $180,000–$200,000 per year, plus performance-related bonuses, with on-target earnings (OTE) of up to $250,000.

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Full-time employment: Opportunity to work on challenging enterprise AI projects with direct business impact.

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Remote work: Primarily remote work, with on-site visits to client facilities and the New York City office approximately once per week.

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Flexible time off: Unlimited vacation policy.

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Comprehensive healthcare: Generous medical, dental, and vision insurance coverage.

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Retirement savings: 401(k) plan with employer matching.

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Advanced AI technology: Opportunities to work with frontier Claude models, AWS services, and production-grade generative AI systems.

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Professional development: Collaboration with experienced AI engineers, access to technical learning opportunities, and support for relevant certifications.

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Technical visibility: Opportunities to contribute to open-source projects, publish technical work, and participate in industry conferences and events.

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End-to-end ownership: Autonomy to shape solutions, influence technical decisions, and see projects through to production.

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Cross-industry experience: Exposure to diverse business challenges and opportunities to build transferable expertise across multiple domains.

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