REQUIREMENTS

Education & Experience:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or related field.
• 3+ years of hands-on experience in machine learning model development and big data analytics.

Technical Skills:

• Proficiency in Python, R, or Scala for data science and machine learning development.
• Experience with machine learning frameworks (TensorFlow, PyTorch, Scikit-learn, XGBoost).
• Strong knowledge of big data platforms (Apache Spark, Hadoop, Databricks) and data pipelines (Airflow, Kafka, etc.).
• Solid understanding of SQL and NoSQL databases.
• Experience with cloud platforms (AWS, Azure, or GCP) for AI/ML deployments.
• Familiarity with MLOps practices (CI/CD for ML, model versioning, monitoring).

Soft Skills:

• Strong analytical and problem-solving skills.
• Excellent communication skills to convey technical concepts to non-technical stakeholders.
• Ability to work collaboratively in a cross-functional environment.

Preferred Qualifications

• Experience with natural language processing (NLP), computer vision, or reinforcement learning.
• Knowledge of real-time data analytics and streaming data pipelines.
• Exposure to business intelligence tools and advanced statistical analysis.

ABOUT THE ROLE

We are seeking an experienced AI Developer with strong expertise in Big Data analytics, machine learning, and AI-driven solutions to join our technology team. The ideal candidate will be responsible for designing, developing, and deploying AI-powered data models, working with large datasets, and enabling data-driven decision-making for business growth and operational excellence.

KEY RESPONSIBILITIES

• AI Model Development – Design, build, and deploy machine learning and deep learning models for predictive analytics, classification, clustering, recommendation systems, and anomaly detection.
• Big Data Processing – Develop and maintain pipelines for processing large-scale datasets from multiple sources using tools like Apache Spark, Hadoop, or cloud-based data platforms.
• Data Engineering – Work closely with data engineers to ensure data quality, accessibility, and scalability for AI applications.
• Model Evaluation & Optimization – Conduct experiments, optimize hyperparameters, and improve model performance for accuracy, efficiency, and scalability.
• Integration – Collaborate with software engineers to integrate AI models into production systems and applications.
• Data Visualization & Reporting – Create dashboards and visual analytics to present insights to stakeholders using tools such as Power BI, Tableau, or custom-built solutions.
• Research & Innovation – Keep abreast of emerging AI/ML technologies, frameworks, and best practices to ensure continuous improvement in solutions.