AI engineer

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