AI Forward Deployed Engineer

As an AI Forward Deployed Engineer, you will work with customers to understand their workflows, technical environments, and business objectives, translating those needs into practical AI‑powered solutions. You will design and build end‑to‑end systems, integrating AI models with customer data sources, APIs, and infrastructure, and deploy these solutions into real operational settings. To be successful, you will need to iterate quickly, troubleshoot complex issues across the entire technical stack, and collaborate closely with internal product, research, and engineering teams to refine core AI capabilities based on field experience. 

Your primary responsibilities will include: 
• Develop AI Solutions: Design and implement AI models to solve complex business problems, selecting relevant features and algorithms to achieve desired outcomes. Deliver demos, proofs of concept, and production‑ready implementations while guiding customers on best practices throughout deployment and adoption. 
• Evaluate Model Performance: Assess the effectiveness of algorithms using relevant metrics, identifying areas for improvement and optimizing model performance. 
• Apply Expertise in Cloud and Data: Support sales and delivery of services across cloud, data, and AI domains, with a strong understanding of complex, multi-practice engagements. Strong proficiency in AWS cloud infrastructure and services (SageMaker, S3, Glue, etc.).
• Experience with Integrated Solution Design: Articulate integrated solutions that span advisory, engineering, and operational capabilities, leveraging expertise across cloud, data, and AI domains.
 • Communicate Results: Clearly articulate the results of AI initiatives, providing actionable insights and recommendations to drive business outcomes.

This job can be performed from anywhere in the US.

• 5–10+ years of recent, hands-on experience in data engineering, machine learning, or AI-related roles
  
• AI/ML expertise: Hands-on experience with AI/ML and Generative AI (LLMs, RAG), including building, fine-tuning, and integrating models into real-world applications
• Agentic AI & workflows: Experience developing or working with agent-based AI solutions (e.g., Amazon Bedrock or similar platforms)
• Software engineering: Strong coding skills (ideally in Python) and experience building production-grade systems on cloud platforms (AWS, GCP, or Azure)
• Data & systems: Working knowledge of SQL, ETL pipelines, and large-scale data processing (e.g., Spark or distributed systems)
• Deployment & tooling: Experience with modern deployment practices (Docker, Kubernetes, CI/CD pipelines)
• Problem-solving: Comfortable navigating ambiguity, prototyping quickly, and turning ideas into working solutions
• Communication: Ability to work with clients and explain technical concepts to non-technical audiences
• Security awareness: Understanding of secure development practices and enterprise considerations for AI systems
• Product mindset: Ability to think end-to-end and design solutions that align with real user needs

• Experience working with senior stakeholders or client leadership
• Background leading or contributing to large-scale AI/ML programs or implementations