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Trailhq

Forward Deployed Engineer

BengaluruIndia
FullTime · Technology
POSTED 17 MAR
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Role
238 days opentrue age
Posted 17 Mar
Pay
₹22.5L–₹29.2Lno pay posted
Market p25–p75
₹22.5L–₹29.2L
Bengaluru · n=5 · 365d to 6 Nov
CompanyTrailhq →
16open roles
Opened · 30d
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Market
37,653open · Software engineering
Opened · 7d
+2,3586% of open
All software engineering
75,184open
All tracked boards · live 10 Nov · n=37,653
LLMsDistributed systems

About Us

Trail is the context layer AI agents run on. We're building the missing layer between your company's rules, exceptions, and institutional knowledge and the agents trying to act on them. It's a governed graph that lets any agent or platform, including Trail agents, make the right call on your most complex business processes. Agents today fail not because models are weak, but because they're never given the judgment a business builds up over years. We're fixing that.

Visit our Website and Youtube to learn more!

About the role

This is a full-time, on-site role for a Forward Deployed Engineer based in Bangalore, India.

In this role, you will work directly with customers to design, build, and deploy AI-driven systems into live production environments. You will operate at the intersection of frontend, backend, and infrastructure, owning problems end-to-end — from initial requirements to production rollout. You will design AI-native interfaces and workflows, integrate LLM-powered systems, and build real-time applications that connect software to physical operations.

Qualifications

2+ years as a Forward Deployed Engineer, software engineer, ML engineer or equivalent — startup experience preferred; founder or founding engineer background is a plus.

Experience building AI-native applications, including: chatbot / conversational interfaces, agentic workflows, RAG systems.

Strong understanding of frontend system design, including: state management (local vs global vs server state), performance optimization, caching, and rendering patterns.

Solid understanding of backend and infrastructure systems, including APIs, data flow, authentication/authorization, and secure data handling — with the ability to debug, design, and collaborate effectively across these layers

Hands-on experience building production LLM systems, including RAG pipelines, agent orchestration, embeddings/vector search, and prompt design — with a strong understanding of trade-offs (latency, cost, hallucinations, reliability)

Ability to own problems and systems end-to-end, from first principles and problem definition through design, implementation, and production.

Strong foundations in computer science and software engineering principles

Experience working in client-facing or high-ownership environments is a plus

Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience)

Source · TrailhqFirst seen 7 NovOriginal ↗