FDE AI/Solutions Architect, AI, Python, Data

Provectus

. Sit with the client and Forward Deployed Executive at engagement start to learn and redesign the function from first principles .

Posted 10/30/2026full-timeRemote • North Macedonia, Montenegro, Moldova, Bulgaria, Czechia, Bosnia & Herzegovina, CroatiaSeniorLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying production GenAI systems, with a strong foundation in AI/ML and proficiency in Python and TypeScript. Capable of leading architectural decisions, optimizing production systems, and effectively communicating with senior stakeholders.

Highest-signal resume keywords

Production GenAI Systems DesignAWS Production DepthAI/ML FoundationsLLM Application DevelopmentArchitectural Trade-Off Decisions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonTypeScriptAI/ML FoundationsProduction RAG SystemsLLM APIsGraph-Based OrchestrationMulti-Step WorkflowsModel MonitoringEvaluation Suite DevelopmentCost and Latency Discipline

Soft Skills

ProactiveSelf-DirectedComfortable with AmbiguityCredible with Senior StakeholdersB2B Collaboration Suitability

Tools & Technologies

AWS BedrockClaude EcosystemContainersCI/CDKubernetesECSLambdaS3SQSECR

Industry Keywords

Insurance UnderwritingHealthcare Revenue CycleAsset FlowBusiness Unit KPIsChange Management

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformKubernetesPythonTypeScript

About the role

Key responsibilities & impact

Sit with the client and Forward Deployed Executive at engagement start to learn and redesign the function from first principles
Reach working fluency in domains such as insurance underwriting, healthcare revenue cycle, and asset flow
Design and ship production GenAI systems in customer environments
Implement and optimize production RAG systems
Build evaluation harnesses, define success criteria, instrument systems, and use evaluations to drive design
Write production code across AI, backend services, and data pipelines
Deploy systems to production on AWS, or GCP/Azure when required, using containers, CI/CD, automated testing, monitoring, and maintainable handover
Start from existing blueprints and feed field learnings back into them
Lead architecture reviews, produce technical design documents, contribute to standards, mentor engineers, and share knowledge
Partner with an FDX accountable for Business Unit KPIs and align technical work to outcomes
Own technical direction for proposals and scoping
Drive adoption and change management
Present outcomes credibly to customer engineers and executives
Shape commitments before they are finalized

Requirements

What you’ll need

B2+ English
Proactive and self-directed; able to identify problems before they are handed to you
Comfortable with ambiguity and ownership
Willing to understand and perform the client's work before coding
Credible with senior stakeholders, including BU heads and CTOs
Able to produce scoped, phased delivery plans with deliverables, dependencies, risks, and cost estimates
7+ years building and running production systems
Solid AI/ML foundations and ability to reason about model failure modes
Experience designing and shipping production LLM applications and agentic workflows
Experience with multi-step workflows, graph-based orchestration, tool use, state management, and partial-failure recovery
Experience with LLM APIs such as Anthropic, AWS Bedrock, or OpenAI and agent frameworks
Experience building and optimizing production RAG systems
Strong engineering fundamentals; proficiency in Python and/or TypeScript
Experience making and defending architectural trade-off decisions
Hands-on AWS production depth, including Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar
Cloud-native delivery experience with containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines
Built or owned an evaluation suite for a non-deterministic system, including ground truth and release gates
Experience with model and agent monitoring and drift detection
Cost and latency discipline, including model tiering and caching
Hands-on production experience with the Claude ecosystem, including Claude Code, CLAUDE.md, hooks, and skills files
MCP knowledge, including why an agent may prefer it to REST integration
B2B collaboration suitability

Benefits

Comp & perks

Remote-friendly culture
Internal training programs with full support for Claude, AWS, and other professional certifications
Conference attendance
Career growth and active engineer development
Access to the latest AI tools and premium subscriptions
Long-term B2B collaboration
Private medical insurance or a budget for medical needs
Paid sick leave, vacation, and public holidays
Equipment and all the tech needed for comfortable, productive work