Gen AI Developer

Key Responsibilities

Build and support high-impact data science and machine learning POCs using Python and modern ML libraries.
Develop and iterate on AI and Generative AI solutions, including experimentation, evaluation, and optimization.
Collaborate with senior engineers and business stakeholders to translate problem statements into ML/AI approaches.
Perform data exploration (EDA), feature engineering, and model performance analysis.
Assist in creating and maintaining end-to-end ML pipelines including training, validation, and basic deployment.
Support model deployment and monitoring for pilot or pre-production environments.
Document solution design, experiments, and outcomes clearly and consistently.

Primary Skills

AI ML & Generative AI Knowledge Expectations
Strong foundational understanding of machine learning concepts, including supervised and unsupervised learning, feature engineering, model training, validation, and evaluation.
Good knowledge of commonly used ML algorithms (linear/logistic regression, decision trees, random forests, gradient boosting, clustering) and appropriate use cases.
Understanding of the end-to-end ML lifecycle, from data preprocessing and experimentation to deployment basics and monitoring.
Solid conceptual and hands-on knowledge of Generative AI and Large Language Models (LLMs), including tokenization, embeddings, prompt engineering, and inference patterns.
Familiarity with LLM-based solution patterns such as summarization, question answering, text classification, chatbots, and retrieval-augmented generation (RAG).
Awareness of model fine-tuning approaches (SFT, parameter-efficient tuning) and when to prefer fine-tuning versus prompting.
Understanding of responsible AI principles, including bias, explainability, data privacy, and model limitations.
Ability to learn, experiment, and apply new AI frameworks, libraries, and cloud AI services with minimal supervision.