Solutions Architect

Forward Deployed Engineer / Solutions Architect - Deccan AI

About:

Deccan AI is a venture-backed AI infrastructure company ($25M Series A, Prosus Ventures), headquartered in the Bay Area with an engineering hub in Hyderabad. We build the infrastructure layer for Reinforcement Learning and Agentic AI and work with frontier labs including Google DeepMind and Snowflake.

Deccan AI builds the data, evaluation, and agentic infrastructure that helps frontier labs and enterprises ship AI that actually works in production. Our Helix and EnterpriseOS platforms put humans and AI side by side to catch failures, close evaluation gaps, and run mission-critical workflows. As we move from data and evals into full-scale enterprise deployments, we're looking for a Forward Deployed Engineer / Solutions Architect to sit at the center of that motion: embedded with customers, owning the technical relationship, and turning ambiguous problems into shipped, reliable AI systems.

This is a customer-facing, build-heavy role. You will spend as much time in a client's codebase and infrastructure as you do in a whiteboard session with their VP of Engineering. You are equally comfortable designing an architecture and writing the code that proves it out.

What you'll do

• Embed directly with enterprise customers to scope, design, and build production AI solutions on top of Deccan AI's Helix and EnterpriseOS platforms.
• Own the end-to-end technical relationship for key accounts: requirements gathering, solution architecture, implementation, deployment, and post-launch iteration.
• Translate ambiguous, often messy customer workflows into concrete technical designs data pipelines, agent architectures, evaluation harnesses, and integration points with the customer's existing infra.
• Write production-grade code alongside customer engineering teams; this is not a slideware role.
• Build reusable patterns, tooling, and accelerators from each engagement so the next deployment moves faster than the last.
• Act as the technical voice of the customer internally feeding field learnings back into product and platform roadmaps.
• Debug and resolve issues in live, high-stakes deployments, often under time pressure and with imperfect information.
• Present architecture, tradeoffs, and rollout plans to both engineering counterparts and executive stakeholders.

What we're looking for

• 4+ years of experience in software engineering, solutions architecture, or a forward-deployed / professional-services engineering role, ideally with exposure to AI, ML, or data-intensive systems.
• Strong hands-on coding ability (Python and/or a modern backend language) you can prototype, productionize, and debug your own work, not just review someone else's.
• Experience designing and deploying systems on at least one major cloud platform (AWS, GCP, or Azure), including data pipelines and API-level integrations.
• Working knowledge of LLM-based systems: prompting, evaluation, RAG, agentic workflows, or fine-tuning you don't need to have built a foundation model, but you need to have shipped something that uses one.
• Excellent communication skills you can move fluidly between a technical deep-dive with engineers and a business conversation with a customer sponsor.
• A bias toward ownership: comfortable being the one person accountable for a customer's technical outcome, without a large team behind you at every step.
• Willingness to travel to customer sites as needed and to work occasionally across customer time zones.

Nice to have

• Prior experience in a forward-deployed engineering, solutions engineering, or technical consulting role at an AI/ML or enterprise software company.
• Experience building or operating evaluation systems for AI agents or LLM applications.
• Familiarity with human-in-the-loop workflows and production monitoring for AI systems.
• Startup experience, particularly in a 0-to-1 or early-scale environment.

Why Deccan AI

• Work at the point where AI research meets real enterprise deployment your work ships to production, not to a slide.
• High-trust, high-ownership culture: capable people who move fast and hold a high bar.
• Direct exposure to frontier AI labs and enterprise customers solving genuinely hard problems.
• Competitive compensation, meaningful equity, and a seat at the table as the company scales.