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

What You’ll Do:

Take the seat

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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.

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Reach working fluency in a new domain — insurance underwriting, healthcare revenue cycle, asset flow.

Build

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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

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Build the evaluation harness before you build the feature. Define what working means, instrument it, and let the evals drive the design.

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Write production code across the stack — AI, backend services, data pipelines. We choose tools to fit the customer.

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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.

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Start from the blueprint, and feed the blueprint. What you learn in the field becomes the baseline the next engagement starts from.

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Lead architecture reviews, produce technical design documents, and contribute to standards. Mentor engineers and share knowledge across the team.

Own the outcome.

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Work in a pair with a FDX who carries the Business Unit’s KPIs. Your work is measured against the same number.

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Own the technical direction of technical proposals and scoping. Drive adoption. Change management is part of the engineering job here.

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Be credible with the customer’s engineers and their executives.

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Shape what we commit to before we commit to it.

What You’ll Bring:

Mindset

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Proactive and self-directed; identify problems before they're handed to you

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Comfort with ambiguity and ownership. Engagements start underspecified by design. Closing that gap is the job

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B2+ English, comfortable collaborating across distributed, multicultural teams

Client Engagement

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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

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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

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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

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7+ years building and running production systems.

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Solid AI/ML foundations. You understand what the models do well enough to reason about failure modes

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Designed and shipped to production LLM applications and agentic workflows — not demos, not POCs, not notebooks

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Agentic orchestration: multi-step workflows, graph-based orchestration, tool use, state management, and recovery from partial failure

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Experience with LLM APIs (Anthropic, AWS Bedrock, or OpenAI) and agent frameworks.

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Experience building and optimizing RAG systems in production

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Strong engineering fundamentals — dropped into an unfamiliar codebase or language, you’re productive. Python and/or TypeScript proficiency; depth matters more than stack.

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Experience in making and defending architectural trade-off decisions

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Hands-on AWS production depth: Bedrock, Bedrock AgentCore, Lambda, ECS, S3, SQS, ECR, or similar. GCP or Azure is a plus

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Cloud-native delivery: containers, ECS or Kubernetes, IaC, and CI/CD applied to AI pipelines

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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

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Model and agent monitoring, drift detection

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Cost and latency discipline: model tiering, caching, and the ability to say what a workload costs to run before it runs

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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

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MCP: you can say why an agent would prefer it to a REST integration. Having authored a server is a plus

Nice to have:

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Prior experience as a founder, CTO, or engineering leader who has chosen to return to individual contribution

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Experience in one of the industries: financial services, insurance, healthcare

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Consulting, professional services, or other embedded customer-facing delivery

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A2A: you can explain agent-to-agent interoperability

- AWS and Claude Code Certifications

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CI/CD pipeline experience (GitHub Actions, GitLab CI)

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Experience in an additional language (Go, TypeScript, or Rust)

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Experience with Apache Spark, Apache Airflow, Kafkа

What We Offer:

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The chance to shape how leading enterprises across LATAM, Europe, and North America adopt AI, from strategy through first deployment

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A forward-deployed model working in small, senior teams alongside Principal Architects and Forward Deployed Engineers

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A growing AI delivery practice where you help build the tooling and frameworks, not just use them

- Remote-friendly culture

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Internal training programs with full support for Claude, AWS, and other professional certifications, conference attendance

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Career growth; we actively develop our engineers

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Access to the latest AI tools and premium subscriptions

- Long-term B2B collaboration

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Private medical insurance or a budget for your medical needs

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Paid sick leave, vacation, and public holidays

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

How we hire:

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Intro conversation. The role, your background and aspirations, tech questions.

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Technical interview with live engineering sessions. Real problems, your own editor, you may use an LLM assistant

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HR Interview. Soft skills and expectations

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HM interview. Tech questions; a live engineering session is also possible

Originally posted on Himalayas