Machine Learning Engineer

Overview

About the Role

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.

Key Responsibilities:

1. 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.g., 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.

2. Business Impact Through ML:

○ 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.

3. 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.

4. Data Warehouse Expertise:

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

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

5. 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.

Qualifications:

● Education: Bachelor's or Master’s degree in Computer Science, Data Science,

Statistics, or a related field.

● Experience:

○ Proven experience in training, deploying, and maintaining ML models in

production.

○ Proficiency in ML libraries and frameworks (e.g., 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.g., 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.

● Skills:

○ Proficiency in SQL for data analysis and manipulation.

○ Strong problem-solving and analytical thinking skills.

○ Familiarity with AWS services (S3, Redshift, SageMaker, Lambda, etc.) is an

advantage.

○ Familiarity with A/B testing and experimentation frameworks.

Job Type: Full-time

Pay: ₹1,500,000.00 - ₹1,800,000.00 per year

Schedule:

• Day shift

Work Location: In person