FDE AI/ Solutions Architect (AI, Python/Data)

What You’ll Do: Take the seat - Sit with the client and the Forward Deployed Executive at the start of an engagement. Learn the function from inside, not from a requirements doc, and redesign the function from first principles. - Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow. Build - Design and ship production GenAI systems into the customer’s environment (cloud-native data, LLM-based, and agentic AI solutions). Implement and optimize RAG systems for production use cases - Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design. - Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer. - Take systems to production on AWS (GCP or Azure where the customer requires it): containerised, CI/CD, automated testing, monitoring, and maintainable after we leave. Hand the system over to the client. - Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from. - Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team. Own the outcome. - Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number. - Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here. - Be credible with the customer’s engineers and their executives. - Shape what we commit to before we commit to it. What You’ll Bring: Mindset - Proactive and self-directed; identify problems before they're handed to you - Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job - B2+ English, comfortable collaborating across distributed, multicultural teams Client Engagement - You are willing to spend time understanding and doing someone else’s job on the client's side before you write a line of code - Credible with senior stakeholders — you can hold a redesign conversation with a BU head and a scoping conversation with a CTO, presenting outcomes to them - You can produce a scoped, phased delivery plan with clear deliverables, dependencies, and risks — and estimate what it will cost to build and to run Technical depth - 7+ years building and running production systems. - Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes - Designed and shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks - Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure - Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks. - Experience building and optimizing RAG systems in production - Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack. - Experience in making and defending architectural trade-off decisions - Hands-on AWS production depth: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus - Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines - You evaluate. You have built or owned an eval suite for a non-deterministic system, and you can explain what you measured, how you produced ground truth, and what gated a release - Model and agent monitoring, drift detection - Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs - Hands-on production experience with the Claude ecosystem — Claude Code, CLAUDE.md, hooks, skills files. Spec-driven development — writing the intent, constraints, and acceptance criteria before you let an agent build — is a strong plus - MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus Nice to have: - Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution - Experience in one of the industries: financial services, insurance, healthcare - Consulting, professional services, or other embedded customer-facing delivery - A2A: you can explain agent-to-agent interoperability - AWS and Claude Code Certifications - CI/CD pipeline experience (GitHub Actions, GitLab CI) - Experience in an additional language (Go, TypeScript, or Rust) - Experience with Apache Spark, Apache Airflow, Kafkа What We Offer: - The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment - A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers - A growing AI delivery practice where you help build the tooling and frameworks, not just use them - Remote-friendly culture - Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance - Career growth; we actively develop our engineers - Access to the latest AI tools and premium subscriptions - Long-term B2B collaboration - 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 How we hire: - Intro conversation. The role, your background and aspirations, tech questions. - Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant - HR Interview. Soft skills and expectations - HM interview. Tech questions; a live engineering session is also possible Originally posted on Himalayas