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Open nowPosted 37 hours ago

FDE - India (Alumni Only)

Interview Kickstart19 open roles

Where
India
Work mode
Hybrid
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Your applicationOpen nowFDE - India (Alumni Only)Interview Kickstart · India

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The clock on this job

Early applications get read.

9.3% of postings close within 7 days. Measured by our own scanner across the market. Interview Kickstart postings stay open a median of 35 days.

Share of postings closed within
  1. 1.8%1 day
  2. 4.0%3 days
  3. 9.3%7 days
  4. 15.6%14 days
  5. 33.9%30 days
This job: posted 37 hours ago

Interview Kickstart median: 35 days open

The posting

ABOUT ACCELER

https://acceler.com/

Acceler is the Forward-Deployed Engineer (FDE) company. We mint and deploy the engineers who take AI from pilot to production — working rapidly and carefully inside your systems.

The expertise behind the work comes from our practitioner network — close to 1,000 engineers, roughly 700 in the US and 300 in India, who do deep AI roles at the frontier today.

WHAT WE DO

AI pilots don't fail on model quality. They fail when capability is split across silos: the builder who ships the system, the translator who frames the problem, and the operator who keeps it running. Our Forward-deployed engineers hold all three.

Our FDEs get inside the company’s stack, on their data, against their constraints. Rapidly, model-agnostic. What ships is a system in production, and a team that can run it after we have gone.

ROLE OVERVIEW

You will work alongside the BU heads to design and build AI-native systems powered by LLMs, AI agents, and intelligent workflows — owning problems end to end, from an ambiguous business need to a production system.

You will start by building for Acceler: the platforms and agents behind our programs and delivery, with real users and real stakes. As these mature, the role extends outward — building alongside our clients on their problems, and in some cases embedding with a client team to deploy and scale the work in their environment, the way our FDE Pods do.

This suits someone who builds quickly without building carelessly — who can prototype in a week, then make the hard calls about what deserves to become production infrastructure.

WHAT WILL YOU BE DOING?

- Own AI agents and autonomous workflows powered by large language models, from problem framing through to production.

- Build RAG pipelines and knowledge retrieval systems, and own their quality as content and usage grow.

- Make the framework and build-versus-buy calls across the orchestration stack, and justify them to engineering and business stakeholders.

- Set the evaluation, observability, and guardrail standards that keep AI systems reliable, and optimize for latency, cost efficiency, and scalability.

- Prototype and launch AI-first product ideas with the BU heads and founding team — tools that improve how our programs are built, delivered, and measured.

- Work directly with client teams as these systems move outward — scoping their problems, adapting what we have built, and deploying it in their environment.

- Raise the engineering bar through design reviews, code reviews, and mentoring on applied AI practice.

- Mode of Work: Remote (for in-house projects)/ Remote with travelling (for Client projects)/ WFO (in case of Client-site deployments)

WHAT WILL EXCITE US?

- 6+ years building production-grade software systems, with at least 2 years on LLM-powered or agentic systems. Any degree from a highly selective institute is preferred. Alternatively, a strong portfolio of projects is required.

- You have shipped Agentic AI systems and LLM-powered applications to real users, and owned them after launch — reliability, latency, and cost.

- Deep hands-on experience with LLM orchestration frameworks — across at least two (LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, or similar), with the judgment to know when a framework is the wrong answer.

- You have designed and tuned Retrieval Augmented Generation (RAG) architectures in production — vector databases, embeddings, chunking, hybrid search, reranking — and can diagnose why retrieval quality degrades at scale.

- Depth in prompt engineering, tool calling, and agent orchestration, and in the evaluation harnesses that keep non-deterministic systems reliable. You have integrated LLMs with external tools, APIs, and structured data sources, and know the failure modes that surface only in production.

- Strong command of backend systems, APIs, and scalable system design, owned end to end — from data modelling through to behaviour under load.

WHY WILL YOU ENJOY WORKING WITH US?

- A cutting-edge AI technology stack, applied to problems with real commercial weight

- Complete ownership and trust from leadership

- Freedom to experiment, fail, and learn

- Rapid career progression for high performers

- A high-caliber team of engineers and industry experts

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