LLM Training

Overview

We are seeking a highly skilled LLM Training & Model Development Engineer with strong expertise in data engineering, model fine-tuning, and optimization. The role focuses on building, customizing, and optimizing home-grown Large Language Models (LLMs) tailored to domain-specific applications. Youll work closely with data scientists, prompt engineers, and infrastructure teams to deliver state-of-the-art AI models that can reason, converse, and adapt intelligently.

Key Responsibilities

• Strong Data Engineer with Agentic AI experience, capable of Data Extract, Transformation, Feature Engineering, Analytics to build AI/ML models
• Curate and preprocess training corpora for domain-specific instruction tuning.
• Fine-tune open-source LLMs using LoRA, RLHF, DPO, and model distillation techniques.
• Implement model evaluation pipelines and benchmark reporting.

• Collaborate with Prompt & Data teams to create repeatable model tuning workflows.

• Data Engineering & Preparation

• Architect and implement data pipelines for large-scale text ingestion, cleaning, and transformation.
  
  • Perform data extraction, transformation, and feature engineering across structured and unstructured sources.
  
  • Develop and maintain data quality frameworks ensuring clean, diverse, and bias-mitigated datasets for model training.
  
  • Automate data labeling and annotation workflows using LLM-assisted or agentic tools.
  
  • Build domain-specific corpora for instruction tuning, conversational grounding, and retrieval-augmented training.

• Model Training & Fine-Tuning

• Fine-tune and adapt open-source LLMs (e.g., LLaMA, Mistral, Falcon, Gemma) using LoRA, QLoRA, RLHF, DPO, and model distillation.
  
  • Implement self-instruct and multi-turn conversational fine-tuning for agentic use cases.
  
  • Design training orchestration scripts for distributed GPU/TPU environments (PyTorch, DeepSpeed, HuggingFace Accelerate).

• Model Evaluation & Benchmarking

• Develop evaluation frameworks for automatic and human-in-the-loop assessment of LLM performance.
  
  • Benchmark models against standard datasets (MMLU, HELM, ARC, TruthfulQA) and custom internal benchmarks.
  
  • Generate detailed performance dashboards tracking precision, hallucination rate, factual consistency, and latency.
  
  • Conduct A/B testing and regression analysis on model updates to ensure stable improvement.

• Collaboration & AI Workflow Automation

• Work cross-functionally with Prompt Engineers, Data Scientists, and DevOps to operationalize model development.
  
  • Build repeatable pipelines for fine-tuning, version control, and continuous model improvement (MLOps).
  
  • Integrate agentic feedback loops for continuous self-improvement and autonomous retraining cycles.
  
  • Support deployment through containerized model serving (FastAPI, Triton, or Ray Serve).

• Research & Innovation

• Stay current with cutting-edge research in LLM fine-tuning, alignment, and model compression.
  
  • Contribute to internal whitepapers and experiments evaluating emerging architectures and optimization methods.
  
  • Prototype and publish novel training methodologies or agentic evaluation techniques.

Required Skills & Experience

• Strong Python expertise with hands-on experience in PyTorch, Hugging Face Transformers, and LangChain.
  
  • Deep understanding of LLM architectures, tokenizer mechanics, and parameter-efficient fine-tuning.
  
  • Proficiency in data processing frameworks (Spark, Airflow, Pandas, Arrow, Dask).
  
  • Experience with distributed training and GPU/TPU optimization (CUDA, NCCL).
  
  • Knowledge of evaluation metrics and human-aligned reward modeling.
  
  • Experience with Vector Databases (FAISS, Milvus, Pinecone) for context retrieval.
  
  • Familiarity with cloud platforms (AWS, GCP, Azure) and container orchestration (Docker, Kubernetes).
  
  • Exposure to agentic AI frameworks and feedback-based continuous improvement systems is a plus.

Preferred Qualifications

• Prior experience contributing to open-source LLM projects.
  
  • Background in NLP research or applied ML.
  
  • Knowledge of data privacy, ethical AI, and prompt alignment techniques.
  
  • Master's or Ph.D. in Computer Science, AI, or related field preferred.

What You'll Get to Build

• A home-grown, domain-specialized LLM trained on proprietary and public datasets.
  
  • A scalable fine-tuning pipeline that powers multiple downstream agents and AI applications.
  
  • An autonomous model training framework capable of learning from feedback in real time.