Job Description

Job Purpose

Design, develop, and deploy machine learning and Generative AI solutions to solve defined business problems, ensuring technical robustness, model performance, and responsible AI implementation.

Key Accountabilities

1. Develop, test, and validate machine learning, deep learning, and Generative AI models (including LLM-based applications).

2. Implement prompt engineering strategies and retrieval-augmented generation (RAG) pipelines.

3. Perform feature engineering, model tuning, and performance optimization.

4. Prepare, cleanse, and transform structured and unstructured datasets.

5. Support deployment of ML and GenAI models using MLOps and LLMOps practices.

6. Conduct structured evaluation of LLM outputs including hallucination detection and quality scoring.

7. Integrate AI solutions via APIs into enterprise systems.

8. Document model design, assumptions, risks, and validation results.

9. Ensure compliance with data governance, cybersecurity, and ethical AI guidelines.

10. Support monitoring, retraining, and lifecycle management of deployed models.

Minimum Qualification, Experience and Competencies

1. Minimum Qualification

• Bachelor’s degree in Data Science, Artificial Intelligence, Computer Science, or a related quantitative field

Minimum Experience

• 4–6 years in data science, machine learning, or applied AI roles.

Skills:

• Python, SQL, ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Prompt engineering
• RAG pipelines & vector databases
• Model fine-tuning and evaluation
• Data preprocessing & feature engineering
• Basic cloud deployment concepts
• MLOps / LLMOps fundamentals
• Analytical problem-solving
• Results Orientation
• Collaboration
• Continuous Improvement
• Accountability