We are seeking a highly skilled and innovative Machine Learning Engineer to join our team.

You will design, develop, and deploy cutting-edge ML solutions to solve real-world problems, driving impactful outcomes for our organization.

Collaborating closely with software engineers and product teams, you will build scalable and efficient ML models and pipelines.

Responsibilities :

Machine Learning Model Development and Deployment :

- Design, build, and optimize machine learning models to solve business problems.

- Deploy trained models to production environments using MLOps practices (e. , CI/CD pipelines, model versioning, and monitoring), ensuring scalability, reliability, and efficiency.

- Continuously monitor model performance and implement improvements to maintain and enhance accuracy.

- Implement and optimize feature engineering workflows, including working with feature stores.

- Leverage ML solutions to improve core business KPIs, including transaction success rates, fraud detection, customer retention, and operational efficiency.

- Work closely with business stakeholders to identify ML use cases aligned with organizational goals.

- Data Engineering and ETL Processes : Design and implement ETL pipelines for efficient data extraction, transformation, and loading.

- Collaborate with data engineers to maintain a robust data pipeline connecting OLTP and OLAP systems.

- Utilize AWS Redshift to manage and analyze large-scale datasets.

- Develop and optimize queries for reporting and feeding ML models.

- Analytical Problem Solving : Apply strong analytical skills to derive insights from data and translate them into actionable recommendations.

- Work with cross-functional teams to interpret data, identify trends, and implement data-driven strategies.

Requirements :

- A skilled ML Engineer with 3+ years of experience in deploying ML models, expertise in AWS, MLOps, and data warehousing, and a strong background in fintech or product-based companies.

Experience :

- Proven experience in training, deploying, and maintaining ML models in production.

- Proficiency in ML libraries and frameworks (e., TensorFlow, PyTorch, Scikit-learn, etc.)

- Experience with cloud platforms like AWS, Azure, or GCP, especially for ML workloads.

- Knowledge of data preprocessing, feature engineering, data warehousing (ie, Redshift), and ETL pipelines.

- Familiarity with MLOps tools and practices (e. , Docker, Kubernetes, MLflow, Sagemaker) would be a plus.

- Strong understanding of statistical methods, algorithms, and performance optimization.

- Experience in the fintech domain is a plus.

- Proficiency in SQL for data analysis and manipulation.

- Strong problem-solving and analytical thinking skills.

- Familiarity with AWS services (S3 Redshift, SageMaker, Lambda, etc.)

- Familiarity with A/B testing and experimentation frameworks.

Must have :

- TensorFlow, PyTorch, Scikit-learn.

- Docker, Kubernetes, MLflow.

- AWS services, SQL, and ETL pipeline