Technical Architect - AI

Role & responsibilities

• Architectural Leadership: You are the go-to person for all AI/ML infrastructure, Agentic workflows, and RAG technology implementations at Lendingkart.
• Strategic Decisions: Play a crucial role in driving AI product and technology decisions, from model selection (SLMs vs. LLMs) to operational deployments.
• Roadmap & Hiring: Show high levels of ownership in driving the AI roadmap, scalable architectures, and hiring top-tier AI and Data Engineers.
• Code & Design Quality: Actively review code, lead design reviews, and guide architectural discussions for multi-agent systems, prompt engineering frameworks, and MCP server integrations.
• Best Practice Adoption: Drive best practices around Generative AI, fine-tuning methodologies (LoRA/QLoRA), model evaluation (Evals), guardrails, and latency optimization.
• Innovation & Experimentation: Experiment with novel AI frameworks, open-source models, and toolings to continuously drive business efficiency and customer impact.
• Tech Ambassador: Represent Lendingkart in external technical forums and AI conferences as a key technology ambassador.

Preferred candidate profile

Core AI & Generative AI Expertise (Hands-On)

• LLMs & SLMs: Practical experience in fine-tuning, serving, and evaluating open-source (Llama, Mistral, Qwen) and proprietary models using LoRA, QLoRA, PEFT, and quantization techniques.
• Agentic AI & Frameworks: Production experience building autonomous, multi-agent frameworks using tools like LangChain, LangGraph, LlamaIndex, AutoGen, or CrewAI.
• MCP Server Integrations: Expertise in integrating Model Context Protocol (MCP) servers/tools to securely expose enterprise context and tools to LLMs.
• RAG Pipelines & Vector DBs: Proven track record of architecting scalable enterprise RAG systems utilizing vector databases (Pinecone, Milvus, Qdrant, PGVector) with advanced retrieval techniques (Hybrid Search, GraphRAG, Query Rewriting, Re-ranking).
• Prompt Engineering & Evals: Mastery of advanced prompt strategies (Chain-of-Thought, ReAct, Few-Shot) and evaluation/observability stacks (Ragas, TruLens, DeepEval, LangSmith, Langfuse).

Engineering, Cloud & Security

• Languages & Frameworks: Highly proficient in Python (PyTorch, Hugging Face, vLLM, Ollama, FastAPI) and backend systems in Golang, Java, or JavaScript.
• Cloud & Cloud-Native: Experience with AWS (Bedrock, SageMaker) and/or GCP (Vertex AI), alongside containerization (Docker, Kubernetes).
• AI Security & Guardrails: Hands-on knowledge of AI guardrails (NeMo Guardrails, Llama Guard, OWASP LLM Top 10), data privacy, PII masking, and red-teaming.
• DevOps & Infrastructure: Experience with CI/CD, Infrastructure as Code (Terraform), and LLMOps monitoring/alerting.