Senior AI/ML Developer

Job Title: Senior AI/ML Developer

Employment Type: Full Time

Position Location: Hyderabad

Reports to: Delivery Head

Qualifications: BE/B.Tech/MCA Degree in Computer Science, Engineering, or similar relevant field

Total Experience: 6 - 10 Years

Working Model: Office

Shift Timing: 12pm 9pm / 1pm to 10pm

About the Role

We are looking for a Senior AI/ML Developer to build and optimize intelligent matching and categorization systems. In this role, you will design and deploy a multi-tiered pipeline that processes unstructured, real-world transactional data. You will leverage a combination of deterministic rules, traditional machine learning, fuzzy matching, and Large Language Models (LLMs) to automatically map incoming records to a standardized master catalog at scale.

Required Experience

• Advanced Python, together with the data and machine-learning libraries around it such as NumPy, Pandas and Scikit-learn.
• Production experience with text classification and entity matching is the core of this seat. That includes string-distance and similarity methods such as Levenshtein, Jaro-Winkler, TF-IDF and cosine similarity, along with vector embeddings, applied to messy real-world product or transaction descriptions.
• Complex SQL against a cloud warehouse comes up daily, and the Snowflake experience needs to cover querying schemas another team owns and controls.
• Production machine-learning practice, covering deployment, drift monitoring, closing a human feedback loop, and tracking confidence across the distribution of results.
• Experience working against a taxonomy or master catalog that changes underneath you, with versioning and a migration story, transfers directly to this seat.

Useful additions

• Experience integrating a large language model for classification or entity extraction, where the input is short, noisy commercial text and the output has to be auditable. It sits here rather than under required experience because whether the engine calls a model at all is still an open decision.
• Retail, grocery, restaurant or foodservice item data shortens the learning curve considerably. Knowing that a sixteen-ounce latte and a large latte are the same item, and that a menu description is not a product name, saves a good deal of time.
• Amazon Bedrock specifically, or any work under an AI control regime with pinned model versions and full prompt and decision logging.