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