ML + Python , LLM, RAG Engineer( 4 yrs )

About The Opportunity

Industry: Enterprise Generative AI & Natural Language Processing (NLP). We build LLM-driven search, knowledge augmentation, and intelligent automation solutions for B2B SaaS and enterprise customers. The team focuses on production-grade Retrieval-Augmented Generation (RAG), embedding pipelines, and low-latency inference services that power customer-facing products and internal automation.Standardized Title: Machine Learning Engineer — LLM & RAG (best-performing title for this search)Role & Responsibilities

• Design and implement end-to-end RAG pipelines: document ingestion, embedding generation, vector indexing, retrieval, and prompt orchestration for production LLM applications.
• Fine-tune, evaluate, and optimize LLMs and embedding models to meet task-specific accuracy, latency, and cost targets.
• Build scalable Python services to expose inference and retrieval through secure REST APIs and microservices.
• Integrate vector databases and search (FAISS/Pinecone/Milvus) and implement efficient nearest-neighbor search, caching, and sharding strategies.
• Containerize and productionize models/services using Docker and orchestration best practices; collaborate on CI/CD, monitoring, and observability for ML workloads.
• Work cross-functionally with Product, Data Engineering, and DevOps to define KPIs, run A/B tests, and iterate on model quality and user experience.
  

Skills & Qualifications

Must-Have (Technical Skills)

• Python
• PyTorch
• Hugging Face Transformers
• LangChain
• Retrieval-Augmented Generation
• FAISS
• Docker
• REST APIs
  

Preferred

• Pinecone
• Milvus
• AWS (SageMaker / EC2) or Azure ML
  

Qualifications

• 4+ years of hands-on experience building ML systems with production deployments in LLM/RAG or NLP applications.
• Proven track record of shipping end-to-end ML features: data ingestion, training/fine-tuning, inference, and monitoring.
• Comfort working remotely across time zones and collaborating asynchronously with engineering and product teams in India.
  

Benefits & Culture Highlights

• Remote-first, flexible-work environment with emphasis on ownership and learning.
• Opportunities to work on cutting-edge LLM projects and influence product direction.
• Competitive compensation, learning stipend, and mentorship from experienced ML engineers.
  

This role is optimized for engineers who combine strong software engineering discipline with deep practical experience in LLMs, vector search, and production ML. If you enjoy turning research-grade models into reliable, scalable services, this is an excellent opportunity to make measurable product impact.
Skills: python,system design,rag,llm