Job highlights

- PhD or Masters in CS/ML with 10+ years in applied AI/ML, expertise in LLM fine-tuning, GraphRAG, and distributed training
- Lead LLM training and fine-tuning, build GraphRAG pipelines, scale multi-GPU training, apply RL alignment techniques, deploy production AI solutions

Role Overview

- Role: Senior AI/ML Engineer
- Experience Required: 10+ years in applied AI/ML with a solid background in statistics and production-grade deployments.
- Education: PhD or Masters degree in Computer Science, Machine Learning, or a related field.

Key Responsibilities

- LLM & Fine-Tuning: Drive end-to-end training and fine-tuning of Large Language Models across both open-source ecosystems (e.g., Qwen, LLaMA, Mistral) and closed-source platforms (e.g., OpenAI, Gemini, Anthropic).
- GraphRAG & Retrieval: Architect and implement GraphRAG pipelines, knowledge graphs, and semantic retrieval systems with advanced document segmentation.
- Infrastructure & Scaling:



Build and scale distributed training environments using NCCL and InfiniBand for multi-GPU and multi-node training.
- Alignment & Reinforcement Learning: Apply techniques such as RLHF, RLAIF, PPO, DPO, and SFT to align model behavior with domain-specific goals.
- Production Deployment: Collaborate with cross-functional teams to translate complex business requirements into production-ready AI solutions.

Required Technical Stack & Expertise

- Core Languages & Tools: Advanced Python coding, SQL, database queries, and Exploratory Data Analysis (EDA).
- Frameworks & Architecture: PyTorch, transformer architectures, state space-based models, and agentic workflows.

- Specialized Skills:
- Vector databases and semantic search (FAISS, Weaviate, Milvus)
- Embedding models (BGE, E5, SimCSE)
- Fine-tuning tools and frameworks (LoRA, QLoRA, axolotl, DSPy, GEPA)
- Cloud platforms (Azure ML and/or AWS)

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