Manager, RAG/LLM Specialist
Hybrid Gurugram Full-time Manager AI Engineering
At a glance
Manager-level AI Engineering role at Fa Ewjt Saasfaprod1. Gurugram · full-time.
Pay not stated AI Engineering salary
RoleAI Engineering
SeniorityManager
LocationGurugram
WorkplaceHybrid
EmploymentFull-time
PostedSep 25, 2026 · 3w ago
Role brief
aimlroles summary, based on the employer's posting.
What you'll do
- Build and run multi-stage RAG pipelines, balancing response speed with retrieval relevance
- Fine-tune open-source models with PEFT or LoRA to improve performance on domain-specific tasks
- Create automated checks for LLM accuracy, context precision and recall
- Set up metadata filters and hybrid search in vector databases such as Pinecone or Milvus
- Coach junior analysts on prompt design, chunking choices and code quality
What you bring
- A bachelor’s or master’s degree in computer science, data science or a related field
- Four to seven years in machine learning or AI, including at least one year focused on LLMs
- Hands-on capability with Python, PyTorch or TensorFlow, LangChain, LlamaIndex and advanced embedding models
- Deep knowledge of advanced RAG methods, prompt optimization and parameter-efficient fine-tuning
Who this fits
This manager role suits someone who can lead RAG and model-tuning work while mentoring junior analysts. It is based in Gurugram, India, with a hybrid arrangement.
From the employer
Role Overview: Lead the design and optimization of advanced RAG pipelines and model finetuning processes. Bridge the gap between prototype and enterprise-scale LLM deployment.
Key Responsibilities
- Pipeline Ownership: Design and manage complex, multi-stage RAG pipelines ensuring low latency and high relevance.
- Model Optimization: Lead fine-tuning initiatives (PEFT/LoRA) for open-source models to
- improve domain-specific task performance.
- Advanced Evaluation: Develop automated evaluation frameworks (e.g., RAGAS) to continually measure LLM accuracy, context precision, and recall.
- Vector Strategy: Architect metadata filtering and hybrid search strategies within vector
- databases (e.g., Pinecone, Milvus).
- Team Mentorship: Guide junior analysts in prompt engineering, chunking strategies, and code quality.
- Tech Stack: Python, PyTorch/TensorFlow, LangChain, LlamaIndex, advanced embedding models.
- GenAI Skills: Deep expertise in advanced RAG (HyDE, parent-document retrieval), prompt optimization, and parameter-efficient fine-tuning.
- Qualifications: Bachelor’s/Master’s in CS/Data Science with 4–7 years in ML/AI, including 1+ years specifically working with LLMs.