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What You’ll Do:
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.
What You’ll Bring:
Mindset
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 .