Required Skills & Qualifications:
Must-Have (Non-Negotiable)
- 2 to 8 years of hands-on experience in ML, AI, or applied data science
- Strong Python and at least one DL framework (PyTorch / TensorFlow / JAX)
- Real production experience with LLMs prompt engineering, fine-tuning, RAG, agent
frameworks (LangChain, LangGraph, CrewAI, or custom)
- Solid grounding in classical ML (regression, trees, ensembles) and modern DL
(transformers, vision, time-series)
- PostgreSQL + vector DBs (pgvector / Pinecone / Qdrant / Weaviate) and pipeline tools
(Airflow, Prefect, or similar)
- Proficiency with AI coding assistants (Claude Code, Cursor, Copilot) - used daily for 2-3x
velocity
- Git, Docker, basic AWS/cloud deployment, and ability to ship features end-to-end
Strongly Preferred
- Experience in healthtech, infra/construction tech, geospatial/CV, or voice AI
- Edge AI / on-device inference experience: TFLite, ONNX Runtime Mobile, CoreML, MediaPipe, NVIDIA Jetson, or similar
- Hands-on with open-source LLMs - fine-tuning (LoRA/QLoRA), quantization (GGUF, AWQ, GPTQ), and serving (vLLM, Ollama, TGI, llama.cpp)
- Knowledge graphs (Neo4j), graph ML, or causal inference
- Computer vision experience (YOLO, SAM, detection/segmentation, OCR)
- Experience with Sarvam AI, Bhashini, or Indic language models
- Time-series forecasting (Prophet, XGBoost, deep forecasting)
- MLOps tooling: MLflow, Weights & Biases, Kubeflow, SageMaker
- Microservices, FastAPI, event-driven architecture (Kafka)
Nice-to-Have
- Open-source contributions, research papers, or public AI projects
- Prior founding-team or 01 product experience
- Domain exposure: clinical workflows, BIM/IFC, BOQ/EVM, road asset standards
- Model serving with ONNX Runtime, Triton, or vLLM