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 skilled 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