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Generative AI Engineer (LLM & Chatbots)

2 years

0 Lacs

pune maharashtra india

Posted:2 weeks ago| Platform: Linkedin logo

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Skills Required

ai stack design model tuning evaluation deployment reliability latency architecture prototype test inference targeting retrieval strategies automate documentation software engineering typescript gcp iam storage orchestration docker kubernetes rest code terraform testing dataset labeling optimization quantization tooling ux websockets security compliance data audit logging healthcare ehr collaboration learning github portfolio

Work Mode

On-site

Job Type

Full Time

Job Description

Role Description


We’re hiring an LLM Engineer to own our conversational AI stack end-to-end—prompt design, RAG, model selection/fine-tuning, evaluation, deployment, and reliability. You’ll build low-latency, high-quality experiences on cloud infrastructure, collaborate closely with product/design, and ship quickly while keeping safety, cost, and maintainability in check.


Location:

Employment:



Responsibilities


  • Architecture & Delivery:

     Design conversational pipelines (RAG, tools/functions, memory) and take features from prototype to production.

  • Prompt Engineering:

     Write, version, and A/B test prompts; implement guardrails, system instructions, and tool-use strategies.

  • Model Ops:

     Evaluate and select models; run SFT/LoRA where appropriate; manage versions, rollouts, and fallbacks.

  • Inference & Performance:

     Deploy and optimize inference on cloud and specialized accelerators (GPU/TPU/LPU), targeting low latency and predictable cost.

  • Retrieval & Data:

     Build ingestion pipelines, chunking strategies, embeddings, and metadata for vector search.

  • Quality, Safety & Monitoring:

     Set up offline/online evals, red-teaming, safety filters, tracing/telemetry, and cost/latency dashboards.

  • MLOps/DevEx:

     Automate CI/CD for prompts, models, and configs; maintain reproducible environments and strong observability.

  • Collaboration:

     Work cross-functionally; write clear documentation; mentor teammates on LLM best practices.



Requirements


  • 2+ years of software engineering (strong 

    Python

    ; bonus 

    TypeScript/Node

    ).
  • 1+ year building 

    production

     LLM applications (not just POCs).
  • Solid with 

    cloud

     (preferably GCP: GKE/Cloud Run, IAM, Storage, Pub/Sub, Secret Manager).
  • Practical 

    prompt engineering

     and 

    RAG

     experience (LangChain/LlamaIndex or equivalent).
  • Vector databases (Pinecone/Weaviate/FAISS), embeddings, retrieval design.
  • Containers & orchestration (Docker, Kubernetes); APIs (REST/gRPC); infra-as-code (Terraform preferred).
  • Strong software practices: testing, observability, incident response, and performance tuning.



Nice to Have


  • Fine-tuning (SFT, LoRA/QLoRA), dataset curation, labeling workflows.
  • vLLM/TGI/TensorRT-LLM; batching, caching, KV-cache optimization; quantization (AWQ/GPTQ).
  • Evaluation tooling (Ragas, DeepEval, Promptfoo) and human-in-the-loop review loops.
  • Realtime/streaming UX (SSE/WebSockets) and speech integrations (ASR/TTS).
  • Security & compliance basics for sensitive data (PII handling, audit logging, RBAC; HIPAA awareness).
  • Healthcare data standards (FHIR/HL7) and EMR/EHR integrations.



How We Work


  • Ship small, measure, iterate; ownership over a focused problem area.
  • Tight collaboration with product/design; pragmatic experimentation.
  • Some overlap with U.S. Eastern time for key meetings (a few evenings IST/week).



What We Offer


  • Competitive salary with performance bonus and equity.
  • Budget for model/inference/eval tooling and observability.
  • Learning stipend and conference support.
  • High impact—your work will shape a flagship conversational AI product.



Application


Send your resume, GitHub/portfolio, and a brief note covering:


  • A production LLM system you built (stack, evals, latency/cost outcomes).
  • Your experience deploying and optimizing inference on cloud and accelerators.
  • A tricky prompt/RAG issue you solved and how you measured improvement.



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