5+ years of hands-on experience developing and deploying AI/ML models in a production setting
Robust proficiency in Python; comfort with SQL and working with large relational and non-relational datasets
Solid grounding in classical ML (regression, classification, clustering, ensemble methods, time-series) and modern deep learning frameworks (PyTorch, HuggingFace)
Hands-on experience with LLMs and Generative AI including prompt engineering, fine-tuning, retrieval-augmented generation (RAG), Graph -RAG and/or building agentic workflows
Experience with MLOps tooling (e.g. MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking, model versioning, and deployment
Familiarity with vector databases (e.g. Pinecone, Weaviate, Chroma)



for semantic search and RAG applications
Excellent problem-solving, communication, and stakeholder management skills
Comfortable working autonomously in a fast-paced, startup-style setting
Exposure to AI agent frameworks (e.g. LangChain, LlamaIndex, CrewAI, or similar)
Experience with cloud ML platforms on AWS (SageMaker), Azure (Azure ML), or GCP (Vertex AI)
Domain Knowledge: A solid understanding of the financial and/or consumer credit market (e.g., credit risk, lending lifecycle, underwriting, or financial time-series data)

📌 Machine Learning Engineer Mumbai
🏢 Gravitas Technology
📍 Mumbai