FDE AI, Solutions Architect – AI, Python, Data

Provectus

. Sit with the client and Forward Deployed Executive at the start of engagements .

Posted 10/30/2026full-timeRemote • Ukraine, Serbia, Armenia, Spain, Slovakia, Slovenia, PolandSeniorLeadWebsite

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, mentoring engineers, and collaborating effectively across multicultural teams.

Highest-signal resume keywords

Production GenAI Systems DesignAWS Production ExperienceAI/ML FoundationsPython and TypeScript ProficiencyArchitectural Trade-Off Decisions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Production Code WritingAI/ML Model ReasoningMulti-Step WorkflowsGraph-Based OrchestrationRAG Systems OptimizationEvaluation Suite DevelopmentCost and Latency OptimizationModel Monitoring and Drift DetectionContainers and CI/CDLLM Application Development

Soft Skills

Proactive Problem IdentificationCollaboration Across TeamsComfort with AmbiguityCredibility with StakeholdersSelf-Directed Ownership

Tools & Technologies

AWS BedrockClaude EcosystemECSKubernetesCI/CD PipelinesGitHub ActionsGitLab CIApache SparkApache AirflowKafka

Certifications & Qualifications

AWS CertificationClaude Code Certification

Industry Keywords

Insurance UnderwritingHealthcare Revenue CycleFinancial ServicesConsultingProfessional Services

Tech Stack

Tools & technologies

AirflowApacheAWSAzureGoogle Cloud PlatformKafkaKubernetesPythonRustSparkTypeScriptGo

About the role

Key responsibilities & impact

Sit with the client and Forward Deployed Executive at the start of engagements
Learn the client's function from inside and redesign it 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 before features; define success criteria, instrumentation, and evaluation-driven design
Write production code across AI, backend services, and data pipelines
Deploy systems to production on AWS, or GCP/Azure when required, with containers, CI/CD, automated testing, monitoring, and maintainability
Start from existing industry Blueprints and feed learnings back into them
Lead architecture reviews, produce technical design documents, and contribute to standards
Mentor engineers and share knowledge across the team
Work with an FDX to own outcomes tied to Business Unit KPIs
Own technical direction of proposals and scoping
Drive adoption and manage change as part of engineering delivery
Present outcomes credibly to customer engineers and executives
Shape commitments before they are finalized
Transfer completed systems to clients

Requirements

What you’ll need

B2+ English
Comfortable collaborating across distributed, multicultural teams
Proactive and self-directed; able to identify problems before they are handed to you
Comfortable with ambiguity and ownership
Willing to spend time understanding and doing the client's job 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
Designed and shipped 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; productive in unfamiliar codebases or languages
Proficiency in Python and/or TypeScript
Experience making and defending architectural trade-off decisions
Hands-on AWS production experience, including Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar
Experience with containers, ECS or Kubernetes, IaC, and CI/CD for 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 optimization skills, including model tiering and caching
Hands-on production experience with the Claude ecosystem, including Claude Code, CLAUDE.md, hooks, and skills files
Understanding of MCP and its advantages over REST integration
Nice to have: founder, CTO, or engineering leadership experience
Nice to have: experience in financial services, insurance, or healthcare
Nice to have: consulting, professional services, or embedded customer-facing delivery experience
Nice to have: A2A knowledge
Nice to have: AWS and Claude Code certifications
Nice to have: CI/CD pipeline experience with GitHub Actions or GitLab CI
Nice to have: Go, TypeScript, or Rust experience
Nice to have: Apache Spark, Apache Airflow, or Kafka experience

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