The Role
Design, train, fine-tune, distill, and evaluate language models; build and maintain RAG pipelines with vector databases; implement LLM observability, monitoring, and evaluation; optimize deployments for latency, cost, and reliability; collaborate to integrate AI into products.
Summary Generated by Built In
We are looking for an AI Engineer with ~2 years of hands-on experience in building, fine-tuning, or distilling language models. The ideal candidate has a strong foundation in Machine Learning and NLP, and is passionate about shipping production-grade AI systems. This role involves working across the full AI stack — from model development to deployment and observability.
🎓 Experience
- 2+ years of professional experience in AI/ML engineering
- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field
✅ Core Requirements (Must-Have)
- Proven experience in at least one of the following: Pre-training or training a small language model from scratch, Fine-tuning large language models (LoRA, QLoRA, full fine-tuning) or Model distillation techniques
- Hands-on experience building RAG pipelines, including vector databases (Pinecone, Weaviate, Qdrant, FAISS), embedding models, chunking strategies, and retrieval optimization
- Strong proficiency in Python and ML frameworks like PyTorch, Hugging Face Transformers, and DeepSpeed or similar distributed training libraries
- Solid understanding of transformer architecture, tokenization, attention mechanisms, and evaluation metrics (perplexity, BLEU, ROUGE, etc.)
📊 LLM Operations & Observability
- Experience with LLM observability and evaluation tools (LangSmith, Weights & Biases, Arize, Helicone, or similar)
- Familiarity with prompt engineering and systematic evaluation of LLM outputs (human-in-the-loop, automated benchmarks)
- Understanding of LLM deployment considerations: latency optimization, caching strategies, token cost management, and rate limiting
✨ Nice to Have
- Experience with agentic AI frameworks (LangChain, LlamaIndex, CrewAI, AutoGen)
- Familiarity with model quantization (GGUF, GPTQ, AWQ) and serving frameworks (vLLM, TGI, Ollama, TensorRT-LLM)
- Exposure to RLHF or DPO (Direct Preference Optimization)
- Knowledge of MLOps practices: CI/CD, experiment tracking, model registries, Docker, Kubernetes
- Experience with cloud AI services (AWS SageMaker, GCP Vertex AI, Azure ML) and GPU infrastructure management
- Contributions to open-source AI/ML projects
🔍 Key Responsibilities
- Design, train, fine-tune, and evaluate language models for production use cases
- Build and maintain RAG pipelines and knowledge retrieval systems
- Implement observability, monitoring, and evaluation frameworks for deployed LLM applications
- Integrate AI into products through collaboration while staying ahead of AI trends and best practices
Skills Required
- 2+ years of professional experience in AI/ML engineering
- Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or related field
- Proven experience with pre-training, fine-tuning (LoRA, QLoRA, full fine-tuning), or model distillation
- Hands-on experience building RAG pipelines, vector databases (Pinecone, Weaviate, Qdrant, FAISS), embedding models, chunking strategies, and retrieval optimization
- Strong proficiency in Python
- Proficiency with ML frameworks such as PyTorch and Hugging Face Transformers
- Experience with distributed training libraries like DeepSpeed or similar
- Solid understanding of transformer architecture, tokenization, attention mechanisms, and evaluation metrics (perplexity, BLEU, ROUGE, etc.)
- Experience with LLM observability/evaluation tools (LangSmith, Weights & Biases, Arize, Helicone, or similar)
- Familiarity with prompt engineering and systematic evaluation of LLM outputs (human-in-the-loop, automated benchmarks)
- Understanding of LLM deployment considerations: latency optimization, caching, token cost management, rate limiting
- Experience with agentic AI frameworks (LangChain, LlamaIndex, CrewAI, AutoGen)
- Familiarity with model quantization (GGUF, GPTQ, AWQ) and serving frameworks (vLLM, TGI, Ollama, TensorRT-LLM)
- Exposure to RLHF or DPO (Direct Preference Optimization)
- Knowledge of MLOps practices: CI/CD, experiment tracking, model registries, Docker, Kubernetes
- Experience with cloud AI services (AWS SageMaker, GCP Vertex AI, Azure ML) and GPU infrastructure management
- Contributions to open-source AI/ML projects
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The Company
What We Do
Statsby Solutions is a pharma-native AI and data company specializing in the pharmaceutical and clinical research industry. They provide end-to-end data and AI capabilities, including AI-powered platforms like Revectra for protocol intelligence and Veractra for Clinical Study Report generation, as well as consulting services in data engineering, Generative AI, and MLOps designed for high-stakes, regulated environments.
