Overview
Strong Data Engineer with Agentic AI experience, capable of Data Extract, Transformation, Feature Engineering, Analytics to build AI/ML models. Look for USA local candidates.
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.
- 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.
- 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).
- 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).
Data & Model Architecture
- 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 data quality frameworks ensuring clean, diverse, and bias-mitigated datasets for model training.
Model Training & Evaluation
- 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.
- Model Evaluation & Benchmarking: Develop evaluation frameworks for automatic and human-in-the-loop assessment of LLM performance; benchmark models against standard datasets and internal benchmarks; generate performance dashboards; conduct A/B testing and regression analysis.
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.