Solution Analysts - Data Science/Artificial Intelligence Engineer

Key Responsibilities

• Design, develop, and deploy predictive models and machine learning algorithms for classification, regression, and clustering.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines and scalable systems using large language models (LLMs).
• Fine-tune and customize LLMs to align with specific business use cases.
• Explore and evaluate open-source LLMs to stay ahead of emerging AI advancements.
• Apply traditional NLP techniques (e.g., BERT, text classification, sentiment analysis, NER).
• Develop and maintain APIs for AI models using frameworks such as FastAPI, ensuring scalability and reliability.
• Collaborate with data scientists, product managers, and engineers to integrate AI-driven features into products.
• Perform data preprocessing, feature engineering, and validation for ML/AI models.
• Document solutions and communicate insights clearly to both technical and business stakeholders.
• Continuously improve models, pipelines, and analytics processes.
  

Technical Skills

• Strong programming skills in Python with libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn.
• Solid experience with SQL for data extraction and transformation.
• Hands-on experience building and tuning supervised & unsupervised ML models.
• Understanding of model evaluation metrics (accuracy, precision, recall, F1-score, ROC-AUC).
• Familiarity with GenAI concepts and production-grade implementation.
• Exposure to traditional NLP or recommendation systems is a plus.
• Knowledge of data engineering practices including cleaning, transformation, and pipeline automation.
• Experience with version control tools (Git) and Jupyter notebooks.
  

• Opportunity to work on cutting-edge AI projects including Generative AI and NLP.
• Collaborate with a team that values innovation, learning, and impact.
• Growth-oriented culture with exposure to diverse projects and technologies.