Forward Deployment Engineer

TENARAI; Bengaluru; On-site; INR 2,000,000 - 4,000,000; Full time

Job summary

TENARAI seeks a Forward Deployed Engineer to build and deploy production-grade AI systems directly with customers in Bengaluru. You will own solutions end-to-end, from discovery through production, and operate code in real customer environments.

This is a builder role requiring hands-on backend work, RAG/orchestration, and strong collaboration with product and platform teams to ensure measurable customer impact.

Qualifications

• 5+ years in software engineering, architecture, or technical delivery.
• Experience shipping production systems in customer environments.
• Hands-on backend and integration engineering; strong debugging skills.
• Experience building LLM applications with tool calling, RAG, orchestration.
• Knowledge of CI/CD, containers, and cloud deployments.
• Security and privacy fundamentals in real implementations.
• Strong communication and stakeholder influence.

Responsibilities

• Design, build, and deploy production-ready AI systems in real customer environments.
• Implement Agentic AI solutions (RAG, orchestration, tool calling, workflows).
• Translate ambiguous customer problems into shippable architectures with clear trade-offs.
• Move solutions from PoC to production with measurable adoption.
• Engineer for enterprise-grade quality: secure, scalable, observable, resilient.
• Lead delivery from discovery through rollout and operational readiness.
• Embed with customer teams to understand tech stack and success criteria.
• Make pragmatic architectural decisions under delivery pressure and evolving constraints.
• Measure success by delivered impact and adoption, not effort or billable time.
• Establish deployment patterns, evaluation loops, and monitoring frameworks.
• Implement reliability patterns: retries, fallbacks, idempotency, safe failure modes.
• Tune systems for performance and economics without sacrificing quality.
• Mentor engineers in production-grade AI delivery practices and reference architectures.
• Collaborate with AI Engineers, platform teams, and research for long-term alignment.
• Provide real-world deployment feedback to strengthen platform capabilities.
• Balance experimentation velocity with enterprise-grade reliability.
• Communicate architecture, risks, and constraints to executives and non-technical stakeholders.
• Frame decisions across scope, reliability, speed, and cost clearly.
• Build trust through transparency, delivery rigor, and measurable outcomes.
• Ensure systems transition to durable ownership with documentation.
• Define monitoring, support, and iteration plans for long-term adoption.

Skills

Backend engineering

APIs

Cloud deployments

LLM applications

Delivery leadership

Security fundamentals

Debugging

RAG/Orchestration

Job description

The Forward Deployed Engineer builds and delivers production-grade, AI-powered systems directly with

customers. This is a builder role: you will write, ship, and operate code in real customer environments.

The FDE bridges product intent, engineering execution, and real-world deployment - owning solutions

end-to-end. From discovery through production, the FDE ensures Agentic AI systems deliver measurable

customer and business impact.

What You'll Do

Design and Deploy Production AI Systems

• Design, build, and deploy production-ready AI systems in real customer environments.
• Implement Agentic AI solutions (RAG, orchestration, tool/function calling, multi-step workflows).
• Translate ambiguous customer problems into shippable technical architectures with clear trade-offs.
• Move solutions from proof-of-concept to production with measurable adoption.
• Engineer for enterprise-grade quality: secure, scalable, observable, resilient.

Own End-to-End Customer Delivery

• Lead delivery from discovery and architecture through rollout, iteration, and operational readiness.
• Embed directly with customer teams to understand tech stack, workflow constraints, and success criteria.
• Make pragmatic architectural decisions under delivery pressure and evolving constraints in the
• Measure success by delivered impact and adoption - not effort or billable time.

Operationalize for Scale

• Establish deployment patterns, evaluation loops, and monitoring frameworks.
• Implement reliability patterns: retries, fallbacks, idempotency, and safe failure modes for agentic workflows.
• Tune systems for performance and economics (latency, throughput, cost) without sacrificing quality.
• Mentor engineers in production-grade AI delivery practices and reusable reference architectures.

Partner Across Product and Platform

• Collaborate with AI Engineers, platform teams, and research to align architecture with long-term direction.
• Provide real-world deployment feedback to strengthen platform capabilities and accelerators.
• Balance experimentation velocity with enterprise-grade reliability.

Influence at Executive and Customer Altitude

• Communicate architecture, risks, and constraints clearly to executives and non-technical stakeholders.
• Frame decisions across scope, reliability, speed, and cost in decision-ready language.
• Build trust through transparency, delivery rigor, and measurable outcomes.

Transition Delivery to Durable Ownership

• Ensure systems transition cleanly into sustained operation with clear ownership and documentation.
• Define monitoring, support, and iteration plans; reduce ambiguity for inheriting teams.
• Support long-term adoption and continuous improvement cycles.

Requirements

• 5+ years in software engineering, architecture, or technical delivery; strong record of shipping production systems.
• Demonstrated customer-embedded delivery: discovery-to-deployment in enterprise
• Hands-on backend and integration engineering (APIs, services, data integrations); strong debugging skills.
• Experience building LLM applications (tool calling, RAG, orchestration) and creating eval/quality gates.
• Production deployment discipline (CI/CD, environments, containers) and cloud experience
• Security and privacy fundamentals (secrets, access controls, PII handling) applied in implementations.
• Strong communication and stakeholder influence; comfortable translating trade-offs to decision makers.