Arcolab - Senior Consultant - Artificial Intelligence/Machine Learning

Job Description :

We are looking for a highly skilled Senior Consultant Machine Learning / AI with 5-8 years of experience in designing, developing, deploying, and maintaining enterprise-grade Machine Learning solutions. The ideal candidate should have hands-on expertise in the complete ML lifecycle, from data preparation and feature engineering to model development, deployment, monitoring, and continuous improvement.

The role requires strong analytical capabilities, proficiency in Python-based ML frameworks, and the ability to work with both structured and unstructured datasets to solve complex business problems through AI-driven solutions.

Key Responsibilities :

• Design, develop, and implement end-to-end Machine Learning solutions for business use cases.

• Build supervised, unsupervised, and reinforcement learning models based on business requirements.

• Develop predictive analytics models for forecasting, classification, clustering, recommendation, anomaly detection, and optimization.

• Select appropriate machine learning algorithms based on problem statements and business objectives.

• Build reusable and scalable ML pipelines for enterprise applications.

• Perform feature engineering and feature selection to improve model accuracy.

• Optimize model performance through hyperparameter tuning and experimentation.

• Develop explainable AI (XAI) solutions wherever required.

• Collect, clean, preprocess, and transform structured and unstructured datasets.

• Handle missing values, duplicate records, outliers, and inconsistent data.

• Perform exploratory data analysis (EDA) to identify trends, patterns, and anomalies.

• Engineer meaningful features for improved model performance.

• Work with large datasets using efficient data processing techniques.

• Implement data balancing techniques for imbalanced datasets.

• Validate data quality before model training.

• Train, validate, and evaluate Machine Learning models using appropriate statistical techniques.

• Build classification, regression, clustering, recommendation, and NLP models.

• Implement ensemble learning methods to improve prediction accuracy.

• Perform cross-validation and model comparison.

• Conduct model benchmarking using suitable evaluation metrics.

• Ensure model scalability and robustness.

• Deploy ML models into production environments.