Job Description
bout the Role
We are seeking a highly skilled
AI/ML Engineer with strong experience in designing, developing, and productionizing enterprise-grade Big Data and Machine Learning solutions. The ideal candidate should have a strong background in statistical modelling, machine learning algorithms, and scalable data engineering.
Key Responsibilities
- Design, develop, and deploy end-to-end Big Data and Machine Learning solutions at enterprise scale.
- Build and maintain Big Data preprocessing and ETL pipelines ensuring consistency, reproducibility, and data quality.
- Work on advanced analytical use cases such as:
- Churn Modelling
- Risk & Fraud Modelling
- Propensity Modelling (Up-sell/Cross-sell)
- Market Basket Analysis
- Recommender Systems
- Time Series Forecasting
- Anomaly Detection
- Segmentation & Campaign Planning
- Implement and optimize statistical and econometric models including GLMs, Bayesian modelling, clustering, causal inference, hypothesis testing, etc.
- Develop and fine-tune classical ML models using Random Forest, XGBoost, SVM, Bagging/Boosting, kernel methods, etc.
- Collaborate with cross-functional teams to integrate ML solutions into production systems.
- Conduct automated testing, model validation, and performance optimization.
Required Skills
AI/ML & Statistical Expertise
- Strong hands-on experience in classical ML algorithms and predictive modelling.
- Deep knowledge of statistical modelling techniques:
- GLMs, clustering, time-series models
- Bayesian modelling, causal modelling, hypothesis testing
- Design of Experiments (DoE)
Big Data Engineering
- Strong experience building Big Data pipelines using:
- PySpark / Spark MLlib
- Hive, Hadoop
- Ability to write efficient, scalable data preprocessing and ETL workflows.
Programming & Fundamentals
- Expert-level proficiency in Python and SQL.
- Solid understanding of data structures, algorithms, design patterns, object-oriented programming, automated testing, and performance optimization.
Cloud & Distributed Systems
- Experience with cloud platforms such as AWS, GCP, or Azure.
- Working knowledge of distributed systems and scalable ML architectures.
Deep Learning (Good to Have)
- Working experience with TensorFlow, PyTorch, and deep learning architectures such as:
- RNNs
- LSTMs
- Transformers
- LLM-based models
SAS Expertise (Added Advantage)
- Hands-on experience with SAS Suite (E-Miner, Financial/Fraud modules) is a plus.
Required Skills
[AI/ML, Data Science, Python]
Additional Information
Who Should Apply
- Candidates with a strong mathematical/statistical background.
- Engineers who have productionized ML models and worked on enterprise-scale Big Data pipelines.
- Immediate joiners or those serving notice.
