Fairdeal.market runs B2B quick-commerce out of dark-store warehouses across Delhi NCR, and our AI/ML systems sit directly in the operational path logistics, computer vision, and agentic tooling that run live every day. We keep the AI team deliberately lean, which means an intern here owns end-to-end problems: framing them, building the model, evaluating it honestly, and shipping it to production. We're looking for someone who thinks in systems, not notebooks comfortable moving from a rough hypothesis to a monitored, versioned service, and disciplined about measuring whether it actually works.
Core Competencies
•Machine Learning Foundations
•Strong Python and a working command of ML theory bias/variance, regularization, evaluation design, and when not
to use ML
•Applied experience with pandas, NumPy, scikit-learn, and at least one deep-learning framework (PyTorch /
TensorFlow)
•Comfort with LLMs and generative AI: RAG architectures, prompt engineering, and grounding techniques
•Fluency with data feature pipelines, preprocessing, and querying (MongoDB / SQL)
•Agentic Systems & Orchestration
•Designing multi-agent and tool-using workflows planning, state management, and controlled tool invocation
•Orchestration frameworks such as LangGraph or LangChain
•Wiring agents to real systems: APIs, databases, and services via MCP or equivalent integration layers
•Model Development & Training
•Building and iterating on models with scikit-learn, XGBoost/LightGBM, PyTorch, or Hugging Face Transformers
•Principled feature engineering, hyperparameter search, and experiment tracking (MLflow / Weights & Biases)
•Fine-tuning and prompt/RAG optimization for LLM-driven use cases
•Evaluation, Testing & Retraining
•Designing evaluation harnesses that reflect the real objective classification metrics for ML, and LLM/RAG evals
(RAGAS, DeepEval, LangSmith) where relevant
•Unit and regression testing for ML code (pytest), with reproducible, deterministic runs
•Monitoring for data and model drift (Evidently) and standing up retraining loops on a schedule or trigger
•Deployment & MLOps
•Serving models as production services — FastAPI, Docker, AWS (Bedrock, SageMaker, Lambda/ECS)
•CI/CD (GitHub Actions), model registries, and dataset/model versioning (MLflow, DVC)
•Production observability: logging, latency and quality monitoring, and safe rollout practices
