Job Purpose
JOB DESCRIPTION
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
- Develop, test, and validate machine learning, deep learning, and Generative AI models (including LLM-based applications).
- Implement prompt engineering strategies and retrieval-augmented generation (RAG) pipelines.
- Perform feature engineering, model tuning, and performance optimization.
- Prepare, cleanse, and transform structured and unstructured datasets.
- Support deployment of ML and GenAI models using MLOps and LLMOps practices.
- Conduct structured evaluation of LLM outputs including hallucination detection and quality scoring.
- Integrate AI solutions via APIs into enterprise systems.
- Document model design, assumptions, risks, and validation results.
- Ensure compliance with data governance, cybersecurity, and ethical AI guidelines.
- Support monitoring, retraining, and lifecycle management of deployed models.
Minimum Qualification, Experience And Competencies
- 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
