Gen AI & Agentic AI Engineer (Klo Job No 146)

Job Title: Gen AI & Agentic AI Engineer

Employment Type: Full-time

Experience: 6–7+ Years

Job Level: Individual Contributor / Owner

Job Summary: This is an individual ownership role — you are expected to design, build, and ship independently with minimal supervision. We are looking for a hands-on Gen AI & Agentic AI Engineer who can independently own end-to-end delivery of intelligent, production-grade AI systems. The ideal candidate is a builder — someone who takes a problem from ideation to deployment without hand-holding, with deep practical experience designing multi-agent architectures, RAG pipelines, and LLM-powered applications at scale.

Key Responsibilities:

• Design and build multi-agent AI systems using frameworks like LangGraph, CrewAI, AutoGen, or equivalent
• Architect and implement RAG pipelines with vector databases (FAISS, Weaviate, Pinecone, ChromaDB)
• Develop and fine-tune LLM-based applications using OpenAI, Claude, Gemini, or open-source models (Llama, Mistral)
• Build and maintain agentic workflows — tool use, function calling, memory systems, orchestration layers
• Implement MLOps/GenAIOps pipelines: model versioning, monitoring, observability, CI/CD for AI systems
• Integrate AI systems with enterprise APIs, databases, and cloud services (AWS / Azure / GCP)
• Own the full delivery lifecycle: requirements fi architecture fi build fi test fi deploy fi monitor
• Evaluate model performance, handle prompt engineering, and optimize for accuracy, latency, and cost

Required Skills:

• Agentic & Gen AI LLMs & RAG
• LangGraph, LangChain, CrewAI / AutoGen, A2A OpenAI / Claude / Gemini, RAG pipelines, Vector Protocol, Multi-agent orchestration, Tool use / DBs, Fine-tuning (LoRA / PEFT), Embeddings, Function calling, Prompt engineering VLLM
• Engineering MLOps / Cloud
• Python, FastAPI, REST APIs, SQL / NoSQL, Docker MLflow, Azure ML / AWS, CI/CD pipelines, Kubernetes, Git workflows Observability & monitoring, Databricks

Nice to Have:

• Experience with real-time document parsing, OCR, or multimodal models
• Exposure to credit risk, finance, or enterprise SaaS domains
• Published work — GitHub projects, technical blogs, or research papers
• Azure Certifications (Data Scientist Associate, AI Engineer Associate)

What We're Looking For:

• Self-starter — takes ownership from brief to delivery without follow-up
• Problem-first thinker — chooses the right tool for the problem, not the trendiest one
• Production mindset — thinks about reliability, observability, and scale from day one
• Communicator — able to explain technical decisions to non-technical stakeholders

Minimum Requirements:

• 6–7+ years of overall experience in ML/AI engineering
• At least 2+ years working specifically with LLMs and agentic frameworks in production
• Proven track record of independently delivered AI products or features