AI Engineer - Model Fine-Tuning & Retrieval Systems

About the Role

AI/ML Engineer

Key Responsibilities

• Design and build RAG-based solutions using vector databases and semantic search.
• Fine-tune open-source SLMs (e.g., Mistral, LLaMA, Phi, etc.) on custom datasets.
• Develop robust training and evaluation pipelines with reproducibility.
• Create and expose REST APIs for model inference using

  FastAPI

  .
• Build lightweight frontends or internal demos with

  Streamlit

  for rapid validation.
• Analyze model performance and iterate quickly on experiments.
• Document processes and contribute to knowledge-sharing within the team.

Must-Have Skills

• 3–5 years of experience in applied ML/AI engineering roles.
• Expert in Python and common AI frameworks (Transformers, PyTorch/TensorFlow).
• Deep understanding of RAG architecture, vector stores (FAISS, Pinecone, Weaviate).
• Experience with fine-tuning transformer models and instruction-tuned SLMs.
• Proficient with FastAPI for backend API deployment and Streamlit for prototyping.
• Knowledge of tokenization, embeddings, training loops, and evaluation metrics.

Nice to Have

• Familiarity with LangChain, Hugging Face ecosystem, and OpenAI APIs.
• Experience with Docker, GitHub Actions, and cloud model deployment (AWS/GCP/Azure).
• Exposure to experiment tracking tools like MLFlow, Weights & Biases.

What We Offer

• Build core tech for next-gen AI products with real-world impact.
• Autonomy and ownership in shaping AI components from research to production.
• Competitive salary, flexible remote work policy, and a growth-driven environment.