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Salary
$27k – $113k per year (Estimated)
Location
In office (Delhi)
Overview
Company
Impact
Profile match
Capri Global Capital Limited is an India based non banking financial company that offers home loans, MSME loans, construction finance and other lending services to retail and business customers in India. It is listed on Indian stock exchanges and operates under the Capri Loans brand with its registered office in Mumbai.

Responsibilities:

  • Design and ship AI-powered product features (LLMs, RAG, agents, ML APIs) into our existing services, working closely with backend, frontend, and data science teams.
  • Integrate off-the-shelf and in-house models (LLMs, embeddings, ML APIs) into robust microservices and user-facing flows.
  • Design and implement RAG and workflow/agent pipelines: retrieval, context assembly, tools integration, guardrails, and fallbacks.
  • Own AI service reliability in production: latency, throughput, cost, observability, circuit breakers, and rollback/versioning of models and prompts.
  • Collaborate with Data Scientists who own model training/finetuning and evaluation design; productionize their outputs as stable APIs/workflows.
  • Implement logging, feedback capture, and lightweight online evaluation hooks to measure quality of AI features over time.
  • Ensure safety, security, and compliance for AI features: prompt injection defences, PII handling, abuse/hallucination controls, and audit trail.
  • Contribute to internal AI tooling: SDKs, templates, and reusable components to accelerate future AI use cases.

Requirements:

  • AI Engineer role demands more than AI-based augmentation, with an in-depth understanding of concepts like RAG, GenAI, LLM fine-tuning, Prompt engineering, multi-agent frameworks (LangChain, LangGraph, etc with hands-on experience), Eval generation and their importance, token usage and optimisations.
  • Strong software engineering in Python (and one of Node/Java/Go),
  • REST/gRPC APIs, queues, and microservices on cloud infra.
  • Hands-on experience shipping at least one AI-powered product to production (e. g., search, recommendations, chatbots, summarisation, classification).
  • Practical knowledge of LLM concepts: prompts, context engineering, embeddings, vector search, basic evaluation metrics, and latency/cost trade-offs.
  • Solid understanding of integration patterns with third-party AI providers (OpenAI, Anthropic, etc. ) and vector DB.
  • Hands-on and good understanding of atleast one agentic framework like Langgraph.
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