About the Role :

We are looking for a hands-on AI/ML (GenAI) Engineer with experience in building, fine-tuning, evaluating, and deploying LLM-powered systems. The role involves working on LLM training and evaluation pipelines, Agentic AI systems, RAG architectures, vector search, and offline/on-premise AI deployments.

Key Responsibilities :

LLM Training & Evaluation :

- Build pipelines for SFT (Supervised Fine-Tuning), DPO (Direct Preference Optimization), and evaluation benchmarking.

- Develop automated evaluation frameworks and test harnesses.

- Perform regression testing and diagnose model issues.

Agentic AI System Development :

- Design agent workflows, planning loops, tool usage, and memory systems.

- Develop multi-step reasoning and AI automation systems.

- Implement prompt engineering and evaluation strategies.

RAG & Vector Search :

- Design and develop RAG pipelines, including ingestion, chunking, and embeddings.

- Work with vector databases such as Pinecone, Weaviate, and Milvus.

- Improve retrieval performance using reranking and evaluation techniques.

Deployment & Engineering :

- Deploy AI/ML systems in offline and on-premise environments.

- Build scalable APIs and services.

- Implement monitoring, logging, debugging, and fallback mechanisms.

Collaboration :

- Collaborate with data scientists and ML engineers.

- Translate business requirements into practical AI solutions.

- Document systems, experiments, and technical implementations.

Required Qualifications :

- Bachelor's degree in Computer Science or a related field.

- 1.5 - 2 years of experience in AI/ML.

- Strong programming skills in Python.

- Hands-on experience with PyTorch, TensorFlow, or JAX.

- Experience with LLM fine-tuning and prompt engineering.

Must-Have Skills :

- Experience building Agentic AI systems and reasoning workflows.

- LangChain, LlamaIndex, LangGraph.

- Strong understanding of RAG.

- Experience building LLM evaluation pipelines.

- Understanding of Transformer models and embeddings.

- Experience deploying solutions in offline/on-premise environments.

- Familiarity with Vector Databases.

- Strong debugging and problem-solving skills.

Why Join :

- Work on advanced LLM and Generative AI systems.

- Build real-world AI solutions.

- Gain hands-on exposure to RAG, LLM evaluation, Agentic AI, and AI deployment.

- Work on challenging AI/ML engineering problems.