Founding Software Engineer

Shyva · Stealth · Remote (India)

Shyva is building the verified trust layer for global trade — an AI-native procurement intelligence platform, in stealth mode.

We're looking for a Founding Engineer who thrives in ambiguity, ships fast, and has a genuine obsession with large-scale data systems. You'll work directly with the founding team to build Shyva's core platform: supplier discovery agents, entity resolution pipelines, semantic search, and the procurement intelligence layer.

This is a 0-to-1 role — broad ownership, direct influence on architecture, and a front-row seat to enterprise AI in global trade.

Applied AI Engineering

●     Shipped production AI products end-to-end — concept, architecture, evaluation, deployment, and ongoing ownership

●     Built AI systems that operate over both structured and unstructured data: retrieval, extraction, reasoning, or workflow automation

●     Designed confidence-aware systems with human-in-the-loop review where the stakes demand it

●     Sharp judgment on where LLMs add value (reasoning, extraction) and where strict deterministic engines must take over (financial calculations, regulatory countdowns, and tariff math)

LLM and Agent Orchestration

●     Shipped multi-step agent workflows in production with modern orchestration frameworks

●     RAG pipelines with hybrid retrieval and reranking

●     Guardrail architecture: post-generation validation, uncertainty flagging, stale-data detection

Search and Retrieval Systems

●     Production experience with modern search systems including vector and hybrid retrieval, reranking, and relevance tuning

●     Experience with knowledge graphs, entity linking, or multi-hop retrieval

●     Strong instincts for retrieval quality, explainability, and trustworthiness

Document Intelligence

●     Production experience extracting structured information from messy unstructured documents

●     Experience with entity resolution, record linkage, or deduplication across noisy real-world data

Large-Scale Data Engineering

●     Production ETL/ELT pipelines at scale

●     Experience ingesting and normalizing heterogeneous commercial data feeds with proper provenance and freshness tracking

●     Data lineage and auditability: every output traceable to source, timestamp, and confidence level

Full-Stack Engineering

●     Python backend mastery, coupled with strong modern frontend skills (React/Next.js, Tailwind). You can translate high-fidelity UI/UX concepts into premium, responsive enterprise dashboards.

●     Cloud-native deployment on AWS or GCP, containerization, CI/CD

Engineering and Systems Ownership

●     Strong software engineering fundamentals; ships production systems end-to-end

●     Comfortable building APIs, workflows, integrations, and pragmatic product-facing systems

●     Experience designing secure, multi-tenant architectures. Understands data compartmentalization, RBAC (Role-Based Access Control), and the technical requirements for enterprise compliance (e.g., SOC2).

●     Owns systems in production — reliability, observability, and the operational calls that come with it

Startup Execution

●     Comfortable operating in ambiguity and moving quickly without fully defined specs

●     Strong ownership mindset with pragmatic decision-making

●     Willing to challenge assumptions and make architectural trade-offs

●     CS, Engineering, or equivalent technical background

●     6+ years of hands-on engineering experience; track record of shipping end-to-end products

●     At least one role where you built something significant without a platform team or DevOps support

●     Recent experience at an early-stage AI startup shipping LLM-native products

●     Supply chain, procurement, or trade finance domain knowledge

●     Background in domains where data accuracy has direct financial or compliance consequences

●     Experience with enterprise system connectors (SAP Ariba, Oracle, or similar)

●     Has built or contributed to open source projects in search, retrieval, or document AI

●     Competitive compensation plus meaningful founding-engineer equity — range discussed with finalists

●     Fortune 500 design partners already committed — you will build for real customers from day one

●     Full architectural ownership: you decide the stack, the data model, the trade-offs

●     Remote, India-based, with at least 4 hours of daily overlap with US Central time

We're not looking for engineers who implement tickets. We're looking for someone who:

●     Has shipped something significant end-to-end and can point to it

●     Reasons about data quality and auditability as a first-class concern, not an afterthought

●     Understands that in enterprise procurement, a wrong number has real financial consequences — and designs systems accordingly

●     Is comfortable making architectural decisions in ambiguity and living with them

●     Has opinions about how to build this and will push back when they disagree

Send your resume and a brief note answering:

●     The most technically complex data system you have built and what made it hard

●     An architectural decision you made with incomplete information and why

●     What draws you to a role where the hardest problems are data quality and trust, not model performance