Forward Deployed Engineer

Forward Deployed Engineer

Company: RVAI Global

Location: Hyderabad / Bangalore, India (with travel to client sites; hybrid)

Function: Client Delivery & Engineering

Reports to: Engineering Leadership

About RVAI Global

RVAI Global is a next-generation AI services company founded in 2024 by Vijay Sivaram and Rohit Himatsingka, on a mission to help global enterprises become AI-led organizations. We work across AI Consulting & Advisory, AI-as-a-Service, Agentic AI Solutions, and AI Talent Solutions, and partner with enterprises to set up dedicated AI-powered Global Capability Centres (GCCs) and Centres of Excellence (CoEs). Following our acquihire of TYNYBAY, we have deepened our agentic AI and AI-native product development capabilities, with a focus on BFSI, Insurance, Healthcare, Retail-CPG, and Telecom.

www.rvaiglobal.com

The Role

Forward Deployed Engineers (FDEs) at RVAI sit at the intersection of engineering, product, and consulting. You will embed directly with our enterprise clients — sometimes on-site, sometimes alongside their internal teams — to translate ambiguous business problems into working agentic AI systems. Unlike a traditional engineering role, you own the outcome end to end: discovery, prototyping, building, deploying, and proving measurable value, often within an 4–8 week initial deployment window.

This is a high-ownership, high-context role for engineers who want to build real production AI systems against real enterprise constraints — and who are equally comfortable in a senior stakeholder conversation and in a terminal.

What You'll Do

• Embed with enterprise clients (BFSI, Insurance, Healthcare, Retail-CPG, Telecom) to understand their workflows, data, and constraints, and identify the highest-leverage AI use cases.
• Rapidly prototype and ship agentic AI solutions — multi-agent systems, RAG pipelines, LLM-integrated workflows, copilots, and AI-native applications — directly inside client environments.
• Own the full lifecycle: requirements, architecture, build, integration with client systems (CRMs, ERPs, data warehouses, internal APIs), evaluation, and production deployment.
• Define and instrument the metrics that prove ROI — accuracy, latency, cost-per-call, deflection, time saved, revenue impact — and iterate against them.
• Act as the technical voice in the room with client engineering, product, and business stakeholders, including senior leadership; translate between business language and technical reality.
• Partner with RVAI's solutioning, talent, and CoE/GCC teams to harden successful prototypes into repeatable offerings.
• Bring back patterns, tooling, and learnings to strengthen RVAI's internal agentic AI platform and accelerators.

What We're Looking For

• 4–9 years of software engineering experience, with at least 1–2 years building production systems involving LLMs, RAG, or agentic frameworks.
• Strong programming fundamentals in Python (TypeScript/Node a plus); comfortable across backend services, APIs, and data pipelines.
• Hands-on experience with one or more of: LangChain / LangGraph, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, or equivalent agent frameworks; familiarity with vector databases (Pinecone, Weaviate, pgvector, etc.) and orchestration patterns.
• Working knowledge of cloud platforms (AWS, Azure, or GCP) and modern deployment practices (containers, CI/CD, basic IaC).
• Demonstrated ability to operate with ambiguity — scoping a problem, deciding what to build, and shipping under time pressure.
• Strong written and verbal communication; ability to lead client conversations from analyst to CXO level.
• Bias toward outcomes over output: you care more about whether the system actually solved the business problem than how elegant the code is.

Nice to Have

• Prior consulting, solutions engineering, or forward deployed experience at an AI/data product or services firm.
• Domain exposure to BFSI, Insurance, Healthcare, Retail-CPG, or Telecom workflows.
• Experience setting up evals, guardrails, and observability for LLM systems (LangSmith, Langfuse, Arize, etc.).
• Familiarity with fine-tuning, model routing, or cost/latency optimization for production LLM workloads.
• Experience helping stand up a CoE, GCC, or AI pod inside a large enterprise.

Why RVAI

• Ground-floor opportunity at a fast-growing, founder-led AI services company building for the agentic era.
• Real problems, real budgets, real production deployments — not demos.
• Direct access to founders and senior leaders with deep operator backgrounds (Quess Corp, Essar/Black Box) and a strong delivery culture from the TYNYBAY team.
• Exposure across industries and geographies, and a clear path into senior technical, solutioning, or practice leadership roles.

This interview will also have an AI screening schedule by RVAI agents