Ability to work independently on projects and processes with close supervision
Broad theoretical knowledge of ML/ AI space and application development using generative AI including supervised fine-tuning, preference optimization (DPO), and reinforcement fine-tuning (RFT) of LLMs; parameter-efficient fine-tuning (LoRA/QLoRA); fine-tuning encoder models such as BERT/ModernBERT for text classification; and retrieval-augmented generation with embedding retrievers and cross-encoder rerankers
Strong grasp of model evaluation methodology (task-specific eval sets, LLM-as-judge, offline metrics, and online A/B testing) and experience building training-data, synthetic-data, and distillation pipelines for post-training
Ability to situationally adapt and understand new technology/processes as per business partner requirement
Strong programming skills in python and fluency in common libraries ( Hugging Face Transformers, TRL, PEFT, Sentence-Transformers, scikit-learn, etc.)
Proficient in SQL and/or other data manipulation languages
Knowledge of big data processing tools such as Apache Spark
Proficiency in version controls systems such as Git
Knowledge of at least one cloud platform (e.g. AWS) and its relevant services (e.g. EMR, S3, and SageMaker)
Ability to interpret business requirements and translate into ML deliverables
Ability to break down and communicate complex, highly technical concepts to audiences of varying technical understanding
Bachelor degree in CS or related field required; Master’s or PhD preferred
8+ years of relevant experience
Experience writing code (e.g. Python) and taking machine learning models to production
Experience building software on cloud computing platforms
Experience deploying, monitoring, and iterating machine learning models in production