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 solid 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

📌 AI engineer (Pune)
🏢 statsby.ai
📍 Pune