Solutions Architect (AI, Python/Data)

• 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.

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