ML/AI Research Engineer — Agentic AI Lab (Founding Team)

Core Responsibilities

• Fine-tune and evaluate open-source LLMs (e.g. LLaMA 3, Mistral, Falcon, Mixtral) for enterprise use cases with both structured and unstructured data
• Build and optimize RAG pipelines using LangChain, LangGraph, LlamaIndex, or Dust — integrated with our vector DBs and internal knowledge graph
• Train agent architectures (ReAct, AutoGPT, BabyAGI, OpenAgents) using enterprise task data
• Create embedding-based memory and retrieval chains with token-efficient chunking strategies
• Create reinforcement learning pipelines to optimize agent behaviors (e.g. RLHF, DPO, PPO)
• Establish scalable evaluation harnesses for LLM and agent performance, including synthetic evals, trace capture, and explainability tools
• Contribute to model observability, drift detection, error classification, and alignment
• Optimize inference latency and GPU resource utilization across cloud and on-prem environments

Desired Experience

• Model Training: Deep experience fine-tuning open-source LLMs using HuggingFace Transformers, DeepSpeed, vLLM, FSDP, LoRA/QLoRA
• Familiar with SFT, RLHF, DPO pipelines
• Worked with both base and instruction-tuned models
• Familiar with building and maintaining custom training datasets, filters, and eval splits
• Understand tradeoffs in batch size, token window, optimizer, precision (FP16, bfloat16), and quantization
• RAG + Knowledge Graphs: Experience building enterprise-grade RAG pipelines integrated with real-time or contextual data
• Familiar with LangChain, LangGraph, LlamaIndex, and open-source vector DBs (Weaviate, Qdrant, FAISS)
• Experience grounding models with structured data (SQL, graph, metadata) + unstructured sources
• Bonus: Worked with Neo4j, Puppygraph, RDF, OWL, or other semantic modeling systems
• Agent Intelligence: Experience training or customizing agent frameworks with multi-step reasoning and memory
• Understand common agent loop patterns (e.g. Plan→Act→Reflect), memory recall, and tools
• Familiar with self-correction, multi-agent communication, and agent ops logging
• Optimization: Strong background in token cost optimization, chunking strategies, reranking (e.g. Cohere, Jina), compression, and retrieval latency tuning
• Experience running models under quantized (int4/int8) or multi-GPU settings with inference tuning (vLLM, TGI)

Preferred Tech Stack

• LLM Training & Inference: HuggingFace Transformers, DeepSpeed, vLLM, FlashAttention, FSDP, LoRA
• Agent Orchestration: LangChain, LangGraph, ReAct, OpenAgents, LlamaIndex
• Vector DBs: Weaviate, Qdrant, FAISS, Pinecone, Chroma
• Knowledge Systems: Neo4j, Puppygraph, RDF, Gremlin, JSON-LD
• Storage & Access: Iceberg, DuckDB, Postgres, Parquet, Delta Lake
• Evaluation: OpenLLM Evals, Trulens, Ragas, LangSmith, Weight & Biases
• Compute: Ray, Kubernetes, TGI, Sagemaker, LambdaLabs, Modal
• Languages: Python (core), optionally Rust (for inference layers) or JS (for UX experimentation)