Contribute to GenAI and agentic solutions, including building components such as prompt pipelines, retrieval systems, and evaluation workflows
Participate in experimentation and innovation initiatives, such as prototyping new approaches and applying emerging AI techniques to business problems
Collaborate with cross-functional teams to integrate models into production systems
Share learnings with peers and contribute to a data science community of practice
Continuously grow technical skills through a structured development plan
Essential Requirements
1. 2 to 4 years of experience (or equivalent) in data science, ML, or analytics with a Bachelor’s or Master’s degree in Statistics, Computer Science, Engineering, Mathematics and experienced in:
LLM tools or platforms (e.g., Azure OpenAI or similar)
Basic RAG pipelines or embeddings
Working with large datasets in production environments
2. Applied Data Science & Solution Delivery
Develop, evaluate, and deploy machine learning and statistical models to solve business problems
Own well-defined problem areas end-to-end, including data preparation, modeling, and performance evaluation
3. GenAI & Emerging AI Techniques
Hands-on implementation of GenAI components and workflows, including
Prompt engineering
Retrieval-augmented generation (RAG)
Basic LLM-based workflows
Assist in developing agentic or multi-step AI workflows under guidance
Evaluate outputs for quality, relevance, and reliability
4. Coding Assist & Code Quality
Use coding-assist tools effectively to accelerate development
Review, debug, and maintain tool-generated code, ensuring quality and correctness
Write clean, well-documented, and testable code following software engineering best practices
5. Modeling & Analytics
Build supervised and unsupervised models including regression, classification, clustering, forecasting, and basic NLP
Perform exploratory data analysis and feature engineering on structured and unstructured datasets
Design and execute experiments (e.g., hypothesis testing, experimentation frameworks), select and tune models to optimize performance.
6. Data & Systems Integration
Query and process data from SQL and unstructured sources
Work with engineering teams to deploy models into production environments
Own model deployment with support from engineering or senior team members
7. Programming & Tools
Strong proficiency in Python
Familiarity with common data science libraries and workflows
Awareness of scalability and performance considerations
8. Innovation & Research
Contribute to innovation through experimentation, prototyping, and applying new techniques
Stay current with emerging trends in ML and GenAI, and apply them where relevant
Participate in team-level research or hackathon initiatives
Desirable Requirements
Exposure to
Model deployment (APIs, containers, or cloud platforms)
Cross-functional collaboration in delivering data products
Familiarity with MLOps / LLMOps concepts. Experience in supply chain, logistics, or operations analytics. Familiarity with MLOps / LLMOps concepts. Participation in innovation initiatives, hackathons, or applied research projects
Who we are
We believe that each of us has the power to make an impact. That’s why we put our team members at the center of everything we do. If you’re looking for an opportunity to grow your career with some of the best minds and most advanced tech in the industry, we’re looking for you.
Dell Technologies is a unique family of businesses that helps individuals and organizations transform how they work, live and play. Join us to build a future that works for everyone because Progress Takes All of Us.
Dell Technologies is committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment. Read the full Equal Employment Opportunity Policy here.
