AI/ML Intern

Role: AI/ML Intern

Experience: 0-1 Yeras

Location: Gurgaon

About the Role

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. Were 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

1. 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)

2. 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

3. 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

4. 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

5. 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

Nice to Have

• Computer vision OpenCV, object detection / segmentation

• Kubernetes or broader cloud infrastructure exposure

• A production system youve shipped and owned, side projects included

What We Expect

• Pursuing or recently completed a degree in CS, IT, Data Science, or a related field

• Self-directed: reads source and docs, debugs independently, and closes loops without hand-holding

• Intellectually honest about results reports what the data shows, not what looks good

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.