Senior AI Engineer

ChatGPT Jobs is seeking a Senior AI Engineer to join Dr.Evidence, an AI-powered insights platform for life sciences. The role involves researching and prototyping ML/NLP architectures, developing scalable training pipelines, and collaborating cross-functionally to enhance AI capabilities.

Responsibilities:

• Research & Prototyping
• Rapidly prototype state-of-the-art ML/NLP/LLM architectures using frameworks like Python, PyTorch, Hugging Face, and LangChain/LangGraph
• Assess open-source and commercial models (e.g., Llama 3, Mistral, GPT-series, Qwen, and other modern LLM architectures) for accuracy, latency, cost, hallucination risk, and compliance
• Run structured model evaluations (accuracy, relevance, precision/recall, hallucination checks)
• Model Development & Training
• Build scalable training pipelines for supervised and self-supervised learning paradigms
• Implement efficient techniques such as LoRA/QLoRA, instruction tuning, and quantization when needed
• Develop agentic workflows (tool-calling, iterative reasoning, ranking, and multi-step pipelines) for real-world use cases
• Improve and maintain existing ML pipelines
• Optimize inference performance with batching, quantization, and efficient serving frameworks
• Retrieval-Augmented Generation (RAG) & Search
• Design and optimize RAG systems that integrate with internal data sources
• Build hybrid retrieval using Elasticsearch/BM25 + vector search (PGVector or other vector DBs)
• Tune chunking, embeddings, reranking, and hallucination-prevention strategies for long documents
• Data Engineering for AI
• Prepare and refine text datasets for training, evaluation, and fine-tuning
• Generate synthetic training data using LLMs to improve extraction accuracy, classification, and reasoning
• Build small, targeted datasets for fine-tuning domain-specific models
• LLM Specialization
• Fine-tune and align open-source LLMs for domain-specific tasks (RAG, agents, tool-calling, reasoning)
• Implement safety layers: prompt guards, output filters, adversarial robustness testing
• Productionization & MLOps
• Containerize models with Docker; orchestrate with Docker Swarm
• Build monitoring for model quality, latency, hallucinations, and regressions
• Collaborate with DevOps and Architecture teams on cost, performance, and scalability decisions
• Implement observability around prompts, retrieval, and model outputs
• Cross-Functional Impact
• Collaborate with Product to define AI roadmaps and success metrics
• Partner with Product and Engineering to design new AI capabilities across modules

Requirements:

• Education: MS/PhD in CS, ML, Data Science, or related field; or BS + 5+ years of intensive AI industry experience
• Core Technical Skills: Languages: Python (expert); familiarity with modern web stacks (e.g., PERN) is a plus
• Experience with PyTorch, Hugging Face Transformers, and common LLM tooling (PEFT, LoRA, QLoRA)
• Strong understanding of NLP: tokenization, embeddings, chunking, NER, classification, summarization
• Experience with RAG pipelines, vector databases, retrieval/ranking strategies
• Experience designing and running model evaluation pipelines
• Comfortable with Docker-based deployments and cloud environments
• Knowledge of Elasticsearch
• Proven Track Record: Shipped at least 3 production LLM-powered features (e.g., chatbots, summarizers, agents)
• Experience working with modern large-scale models
• Experience designing clean, maintainable architectures for LLM features
• Strong documentation and communication skills