Data Science Engineer - AIML & LLM

We are seeking highly qualified and innovative Data Scientists to join our ambitious Product & Program Definition Transformation team. In this role, you will be central to developing the intelligent systems that power our move to a future-state PLM environment.

You will apply your expertise to solve complex, real-world problems at the heart of our engineering and business processes, directly contributing to the development of our Intelligent Transition Model and a suite of AI-Powered Accelerators.

Your primary responsibilities will include :

• Designing and building scalable AI / ML models to automate the decomposition and transformation of legacy artifacts (e.g., Program Direction Letters, Order Guides, BOMs) into the future-state data model.
• Developing analytics prototypes and accelerators that work on massive, diverse datasets to provide actionable insights for all project workstreams (e.g., VSM generation, relationship analysis, complexity metrics).
• Partnering with business and architecture teams to translate complex, often ambiguous, business logic into robust, automated solutions.

Required Skills and Experience :

1. AI & Machine Learning Expertise : Generative AI & LLMs :

This includes expertise in advanced prompt engineering, model fine-tuning, and leveraging LLM APIs to automate the decomposition of complex business artifacts and unstructured text.

2. Natural Language Processing (NLP) & Text Mining :

3. Classical Machine Learning : Strong foundation in applying traditional ML algorithms such as clustering, classification, decision trees, random forests, and support vector machines.

Data & Programming Skills :

Data Processing and Wrangling :

Programming Languages :

Big Data & Cloud Technologies (GCP) :

Inquisitive and Tenacious Mindset :

Skills Required :

Skills Preferred :

Experience Required :

1. Generative AI (GenAI) and Large Language Models (LLMs) : Proven expertise in leveraging GenAI, with hands-on experience in prompt engineering, model fine-tuning, and using LLM APIs for complex data processing, text mining, and automation.

2. Deep Learning : Proficiency in deep learning principles, including the design and implementation of neural networks, reinforcement learning, and an understanding of transformer architectures.

3. Classical Machine Learning : Strong foundation in traditional ML algorithms such as clustering, classification, decision trees, random forests, and support vector machines for predictive modeling and data analysis.

Experience Preferred :

3+ years of experience in at least one of the following languages : Python, R, MATLAB, SAS Experience with GoogleCloud Platform (GCP) including VertexAI, BigQuery, DBT, NoSQL database and Hadoop Ecosystem

(ref : hirist.tech)