Forward Deployed AI Engineer, GenAI, AWS

Provectus

Forward Deployed AI Engineer at Provectus, specializing in applied AI solutions for enterprises. Embedding in client operations to drive measurable business outcomes through AI.

Posted 7/22/2026full-timeRemote • Connecticut, Massachusetts, New Jersey, New York, North Carolina, Pennsylvania, Virginia • 🇺🇸 United StatesSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates strong engineering fundamentals with hands-on experience in building and deploying GenAI/LLM systems in production. Proficient in Python and/or TypeScript, with a solid understanding of cloud-native delivery on AWS and the ability to engage effectively with senior stakeholders in financial services, insurance, and healthcare domains.

Highest-signal resume keywords

GenAI/LLM Systems DeploymentCloud-Native Delivery on AWSPython and/or TypeScript ProficiencyStrong Engineering FundamentalsStakeholder Engagement

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

GenAI SystemsLLM SystemsPythonTypeScriptCloud-Native DeliveryContainersKubernetesCI/CDMLOpsData Analytics

Soft Skills

Comfort with AmbiguityStakeholder CommunicationDomain Learning

Tools & Technologies

AWSGCPAzureKubernetesTerraformSageMakerMLflowNeo4jAWS NeptuneAWS CDK

Industry Keywords

Financial ServicesInsuranceHealthcareData GovernanceData Quality

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformKubernetesNeo4jPythonPyTorchTerraformTypeScript

About the role

Key responsibilities & impact

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

What you’ll need

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

Comp & perks

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