Role PurposeThe 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 customer stack.

- 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.

What an FDE Does Not Do

- Does not stop at a demo - the expectation is shippable, production behavior in customer environments.

- Does not operate as a strategy-only advisor - success requires building and shipping working systems.

- Does not specialize only in UI/UX - frontend is a plus, but backend/APIs/integrations are foundational.

- Does not work in isolation - partners with platform, account, and execution teams to land outcomes.

- Does not run project management day-to-day - collaborates with delivery leads while owning the technical delivery outcomes.

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 environments.

- 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 (AWS/Azure/GCP).

- Security and privacy fundamentals (secrets, access controls, PII handling) applied in implementations.

- Strong communication and stakeholder influence; comfortable translating trade-offs to decision-makers.

Experience Snapshot Category Expectation

- 5+ years in engineering, architecture, or technical delivery

- Production systems shipped

- Multiple production deployments; evidence of operating and supporting systems post-launch

- Client environment experience

- Direct experience delivering in customer settings; comfortable embedding with teams

- Enterprise integration

- APIs, identity, data sources, and legacy integration patterns





- Hands-on technical background

- Required (backend/APIs/integrations; frontend a plus to support end-to-end delivery)

- Fast-paced exposure

- Startup, product, or high-velocity delivery environments strongly preferred

Skills

Primary Skills

- Backend or full-stack engineering (production systems, APIs, integrations)

- System design and architecture (trade-offs, scalability, resiliency)

- LLM application engineering (tool calling, RAG, orchestration)

- Evaluation and quality gates (offline evals, regression, safety checks)

- Production deployment and operational readiness (CI/CD, observability, incident response)

- Customer-embedded technical delivery (discovery, build, deploy, handoff)

Additional Skills

- Vector databases and retrieval systems; hybrid retrieval and reranking concepts

- Cloud infrastructure and platform services (AWS/Azure/GCP)

- Monitoring, tracing, and LLM/agent telemetry tooling

- Security, privacy, and responsible AI implementation patterns

- Frontend development (React or similar) to support end-to-end experiences

Outcomes and Key Deliverables

- Production deployment (or a defined MVP scope) running in the customer environment - not a demo-only artifact.

- Integration completed with required systems (APIs, identity, data sources) and validated end-to-end workflows.

- Evaluation harness with baseline quality metrics and regression gates (accuracy, grounding, safety).

- Observability pack: traces, dashboards, and alerts for reliability, latency, and cost-per-outcome.

- Security and privacy checklist completed (secrets, access controls, PII boundaries, prompt-injection defenses).

- Runbook and handoff package: operating procedures, support plan, and backlog for the inheriting team.

- Reusable patterns captured as accelerators/playbooks to improve future deployments.

Success Measures

- Time-to-first-value in the customer environment (days/weeks, not months).

- Adoption and usage of the shipped workflow (active users, runs per week, completion rate).

- Reliability and supportability (incident rate, mean-time-to-recovery, rollback readiness).

- Quality and safety (eval pass rates, grounding/citation correctness where applicable).

- Performance and economics (p95 latency, cost-per-outcome within an agreed budget).

- Clean transition to durable ownership (runbook usage, reduced ambiguity, reliable operations).

📌 Forward Deployment Engineer (Bengaluru)
🏢 TENARAI TECHNOLOGIES PRIVATE
📍 Bengaluru