About the Role
Build, tune, evaluate and serve our own small language and ML models in-house. Role will own the science end to end, base model to production, with the evals and model cards to prove it.

Key Responsibilities:
. Train, fine-tune and distil models-genAI (LoRA/PEFT, RLHF, prompt-based) and classical (transfer learning)-for live product use cases.
. Design evaluation, model tracking and model cards: rubrics, LM-as-judge, regression, drift.
. Own the tune vs. swap the base model call partner with platform to deploy, serve and maintain foundry models.
. Set method and mentor the DS/ML team.

Role Proficiency:
. Fine-tuning/PEFT, transfer learning, RLHF, prompt tuning evaluation and experiment tracking.
. Strong probability and statistical inference-distributions, Bayesian methods, significance, calibration, experiment/A-B design and sound handling of skewed/imbalanced data (resampling, class weighting).
.



Some MLOps- deploy/serve/monitor (SageMaker, MLflow, Docker vLLM/Triton a plus).
. MS/PhD in CS/Stats preferred. Fluent in Python/Jupyter, PyTorch, Hugging Face SQL/Postgres (pgvector), a vector store, a warehouse (Snowflake/BigQuery) Spark or Ray Airflow/dbt.
. Shipped many models in a large org robust written and verbal communication skills (tech blogs, publications, talks at meetups/conferences).

Qualifications:
Post-graduate degree (MBA, MTech, LLM, or equivalent) from a top-tier institution.
6+ yrs building ML/AI in production, deep in model training-genAI or classical (at least one both preferred)-with 3+ yrs leading or tech-leading.

Good to Have:
GenAI product experience (e.g. enterprise RAG bot) text, voice and vision (VLM) models startup / fast-paced background

📌 EAIG:Head - Data Science (Bengaluru)
🏢 Axis Bank
📍 Bengaluru