Machine Learning Engineer - Tensorflow/PyTorch (3-6 yrs)

Job Description :

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.

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.

Responsibilities :

- 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.

- 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.

- Data Warehouse Expertise : 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. 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.

- 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.