Solutions Architect (GenAI, Python/Data, AWS)

Provectus
3 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Remote

Tech stack

Multitier Architecture
Artificial Intelligence
Airflow
Amazon Web Services (AWS)
Amazon Web Services (AWS)
Automation of Tests
Cloud Computing
Cloud Engineering
Code Review
Continuous Integration
ETL
Software Design Patterns
Distributed Systems
Django
Github
Monitoring of Systems
Python
Machine Learning
Node.js
Object-Oriented Software Development
Performance Tuning
Software Tools
Cloud Services
Systems Integration
Flask
Large Language Models
Spark
Backend
FastAPI
Build Management
Gitlab-ci
Machine Learning Operations
Virtual Agents
REST
Amazon Web Services (AWS)
Docker

Job description

  • Design and build cloud-native data, LLM-based, and agentic AI solutions addressing real client business challenges

  • Implement and optimize RAG systems for production use cases

  • Build and maintain strong relationships with key customer stakeholders, acting as a trusted technical advisor.

  • Support presales: discovery calls, technical proposals, scoping, and client-facing demos

  • Own the technical direction of client engagements from discovery through delivery - the go-to authority for clients and the internal team

  • Write clean, production-grade Python across AI integrations, backend services, and RESTful APIs

  • Build and maintain ETL/ELT workflows using modern orchestration and distributed computing tools.

  • Deploy ML and LLM-based solutions

  • Implement MLOps, LLMOps, and AgentOps practices: CI/CD, automated testing, model monitoring, and experiment tracking.

  • Lead architecture reviews, produce technical design documents, and contribute to standards

  • Mentor engineers, lead code reviews, and share knowledge across the team., * Internal training programs (Leadership, Public Speaking, and more) with full support for AWS and other professional certifications

  • Career growth: a clear path toward SA or beyond; we actively develop our engineers

  • Access to the latest AI tools and premium subscriptions

  • Long-term B2B collaboration

  • Remote with flexible hours

  • Private medical insurance or a budget for your medical needs

  • Paid sick leave, vacation, and public holidays

  • Equipment and all the tech you need for comfortable, productive work

Requirements

Do you have experience in Spark?, Do you have a Master's degree?, * Full-stack mindset, comfortable across AI, backend development, and cloud infrastructure

  • Already using AI tools in your daily workflow (Claude Code, Copilot, or similar)
  • Proactive and self-directed; you own outcomes end-to-end and spot problems before they're handed to you
  • B2+ English, comfortable collaborating across distributed, multicultural teams

Presales & Client Engagement

  • Owns the client technical relationship; leading discovery, decomposing ambiguous requirements into technical components, presenting architecture, and pushing back on scope when it doesn't match timeline or budget
  • Produces scoped, phased delivery plans with clear deliverables, dependencies, and risks
  • Experience with cost estimation and cloud architecture cost optimization

AI & Python/ Data & Cloud

  • 7+ years building and running production systems - not only demos and POCs
  • Hands-on experience building production LLM-based applications and agentic workflows
  • Experience in integrating AI/ML components into solutions
  • Experience with LLM APIs (OpenAI, Anthropic, or AWS Bedrock)
  • Experience building and optimizing RAG systems
  • Understanding of LLM evaluation techniques and quality assurance approaches
  • Experience deploying and maintaining AI/ML models in production environments
  • Python skills: OOP, design patterns, clean architecture, and performance optimization
  • Experience building RESTful APIs with FastAPI, Django REST, or Flask
  • Experience in making and defending architectural trade-off decisions
  • Experience with Docker and Kubernetes
  • Hands-on experience with AWS (Bedrock AgentCore, Bedrock, Lambda, ECS, S3, SQS, ECR, or similar); GCP considered
  • Understanding of CI/CD practices applied to ML and AI pipelines
  • Familiarity with model monitoring, observability, and drift detection.

Nice to Have

  • AWS and Claude Code Certifications
  • CI/CD pipeline experience (GitHub Actions, GitLab CI)
  • Experience in an additional language (Go, Node.js, or Rust).
  • Hands-on experience with Apache Spark, Apache Airflow, Kafkа

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