AI & ML

What you’ll do

• Explore data, build features, and train/evaluate models (classification, regression, recommender, time-series).
• Productionize models (APIs/batch jobs), monitor drift/latency, and iterate quickly.
• Partner with product/ops to define measurable problem statements and success metrics.
• Write clean, tested Python; review PRs and contribute to shared libraries.
• Use MLOps tools for experiment tracking, CI/CD, and model registry.
• Optimize cost/perf on cloud.

Must-have

• Strong Python; NumPy/Pandas; scikit-learn; SQL.
• One deep-learning stack: PyTorch or TensorFlow/Keras.
• Experience deploying to production (Docker + FastAPI/Flask, or Spark jobs).
• Version control (Git), experiment tracking (MLflow/W&B), basic stats.
• Cloud familiarity (AWS/GCP/Azure).

Nice-to-have

• Feature stores, model monitoring (Evidently/Fiddler).
• Basics of LLMs/RAG, vector DBs (FAISS/Pinecone).
• Streaming (Kafka), orchestration (Airflow), GPUs.

KPIs

• Uplift in target metric (e.g., +AUC/+CTR/–MAE).
• Serving latency & uptime; retraining cadence.
• Cost per 1k predictions.

Comp & perks: [CTC range], ESOPs [Y/N], insurance, learning budget.
Apply: [email/careers link] with GitHub/portfolio.`