Forward Deployed AI Engineer, GenAI, AWS

🕒 July 22

🦌 Connecticut, New Jersey, +5 more states – Remote

🦌 Connecticut – Remote 🏖️ New Jersey – Remote 🗽 New York – Remote 🌲 North Carolina – Remote 🍂 Massachusetts – Remote 🔔 Pennsylvania – Remote ⚔️ Virginia – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

🤖 AI Engineer

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Provectus

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2012

💼 Consulting

🏥 Healthcare

🏭 Manufacturing

Consulting • Healthcare • Manufacturing

Provectus is an artificial intelligence consultancy and solutions provider that helps businesses transform through AI. Offering both a use case and a platform approach, Provectus integrates AI into organizations to achieve unique business objectives and technical capabilities. Their solutions are cloud-native, vendor-agnostic, and open, allowing for deployment in customer's cloud without restrictive licenses. With applications in industries like retail, manufacturing, and healthcare, Provectus delivers AI-powered use cases and turnkey solutions to drive innovation and efficiency. They also offer consulting, customization, and managed AI services.

📋 Description

• Provectus is a Premier AWS partner and an Anthropic Strategic Partner at the forefront of applied AI, helping enterprises turn Claude, agentic systems, and their own data into measurable business outcomes. • Work centers on Financial Services & Insurance and Healthcare & Life Sciences deploying five pre-built AI Blueprints. • Embed engineers and leaders inside client operations as Forward Deployed Engineers (FDE). • Spend the first weeks of an engagement in the operator’s seat to learn the work and rebuild the function from first principles. • Be measured on whether the Business Unit’s number moved, not on hours or scope delivered. • This is a role for engineers who have led before and want to stay in the code while owning the outcome.

🎯 Requirements

• 8+ years building software, a substantial share of it writing production code you were accountable for. You are hands-on today and intend to stay that way. • You will take the operator’s seat. You are genuinely willing to spend weeks doing someone else’s job — claims processing, underwriting, revenue-cycle work — before you write a line of code. Engineers who need to stay in the IDE should not apply. • You learn domains fast. Demonstrated ability to become conversant in an unfamiliar business function quickly enough to argue with the people who do it for a living. • Shipped GenAI/LLM systems to production — not demos, not notebooks. You’ve handled the parts that get hard after the prototype works. • You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured and why. • Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack. • Cloud-native delivery on AWS (GCP/Azure a plus): containers, Kubernetes/ECS, IaC, CI/CD, and the operational reality of a system someone else inherits. • Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO without losing either room. • Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job. • Solid AI/ML foundations — you understand what the models do well enough to reason about failure modes, not just call the API. • Fluent English, written and spoken. • Nice to have: • Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution. • Real depth in one of our blueprint industries: financial services, insurance, healthcare, asset management. • Consulting, professional services, or other embedded customer-facing delivery. • Data platform depth: data lakes, warehouses, streaming and real-time analytics, data mesh and data contracts, governance and data quality. • MLOps and classical ML: PyTorch, SageMaker, MLflow. • Fine-tuning, distillation, or inference/serving optimization. • Graph databases (Neo4j, AWS Neptune). • IaC depth: AWS CDK, CloudFormation, Terraform. • Open-source contributions or public writing on applied AI.

🏖️ Benefits

• Frontier delivery work across Cowork Activation, Agentic SDLC, and Blueprint Activations in Financial Services and Healthcare • The chance to shape how leading enterprises adopt AI, from strategy through first deployment • A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers • A growing AI delivery practice where you help build the tooling and frameworks, not just use them • Remote-friendly culture