LLMOps Engineer

About LenDenClub

LenDenClub is India's largest Peer-to-Peer (P2P) Lending platform, owned and operated by Innofin Solutions Pvt. Ltd., an RBI-registered (NBFC-P2P). It provides an opportunity to lenders looking for high interest rates by connecting them with creditworthy borrowers looking for short-term loans. LenDenClub has become a go-to platform to earn returns, lenders have historically earned an XIRR 11.28% p.a. The platform has an active user base of over 2 Cr+ users and loan disbursal of over 15,000 crores.

Why work at LenDenClub

At LenDenClub, we take pride in being the first Peer-to-Peer lending platform to offer ESOP liquidity, and this achievement has allowed us to set a new benchmark. The progressive approach towards employee benefits has been acknowledged and appreciated and as a result, LenDenClub has been certified as a 'Great Place to Work' successively for four years by the Great Place to Work Institute, Inc. As a LenDenite, you are a part of an enthusiastic and passionate group of individuals who own and love what they do. At LenDenClub, we believe in creating leaders, and you get to work with complete freedom to chase your ultimate career goal without any inhibitions. We remain committed to continuing our journey of innovation and fostering a positive work environment for our team.

Position: LLMOps Engineer

LLMOps Engineer

Responsibilities:

• Manage full LLM lifecycle:

  data preprocessing, fine-tuning, evaluation, deployment, retraining

  .
• Deploy & scale

  large-weight LLMs

  with

  distributed inference, GPU optimization, quantization

  and caching.
• Build

  RAG pipelines

  with embedding generation, vector DBs (FAISS, Pinecone, Weaviate, Milvus) and retrieval strategies.
• Implement

  observability frameworks

  for

  latency, token usage, hallucination detection, guardrails, cost tracking

  .
• Integrate LLMs with

  APIs, microservices, and enterprise workflows (LangChain, LlamaIndex, Triton Inference Server)

  .
• Continuously optimize

  GPU utilization, inference throughput and cost efficiency

  .

Requirements:

• Strong knowledge of

  LLM deployment, RAG architectures, embeddings, multimodal pipelines

  .
• Expertise in

  AWS GPU infra (EKS, Inferentia, Trainium), caching and distributed inference

  .
• Experience with

  monitoring frameworks

  .
• Proven track record of

  scaling enterprise LLM applications with compliance & observability frameworks

  .