Job Title : Machine Learning Engineer

Experience Required : 4+ years of hands-on experience in Machine Learning and applied data science

About Us :

At Codvo, we are committed to building scalable, future-ready data platforms that power business impact. We believe in a culture of innovation, collaboration, and growth, where engineers can experiment, learn, and thrive. Join us to be part of a team that solves complex data challenges with creativity and cutting-edge technology.

Role Overview :

We are looking for an experienced Machine Learning Engineer who can work on data-heavy, real-world business problems, build robust ML models, and take them end-to-end from data manipulation to production deployment. The role requires strong fundamentals in statistics, machine learning, and data engineering, with practical exposure to scalable systems.

Key Responsibilities :

- Perform data manipulation, cleaning, preprocessing, and transformation on large structured datasets

- Analyze datasets to identify patterns, correlations, anomalies, and data quality issues

- Design, develop, and optimize machine learning models for business and operational use cases

- Apply feature engineering, feature selection, and dimensionality reduction techniques

- Train, evaluate, and tune models using appropriate metrics and validation strategies

- Deploy machine learning models into production environments and monitor performance

- Collaborate with data engineers, software engineers, and stakeholders to deliver scalable ML solutions

- Document models, assumptions, and results for technical and non-technical audiences

Must-Have Technical Skills :

Programming & Data Handling :

- Strong proficiency in Python

- Extensive experience with data manipulation and analysis using :

1. Pandas

2. NumPy

- Strong ability to work with large datasets and structured data

Databases & Querying :

- Proficiency in SQL for :

1. Data extraction

2. Data aggregation

3. Analytical queries

Core Machine Learning :

- Hands-on experience with :

1. Regression

2. Classification

3. Clustering

4. Time Series Forecasting

- Strong understanding of :

1. Feature engineering and feature selection

2. Model evaluation metrics (RMSE, MAE, ROC-AUC, Precision, Recall, F1-score)

- Cross-validation and hyperparameter tuning

- Biasvariance tradeoff

Machine Learning Frameworks :

- Strong hands-on experience with Scikit-learn

- Working experience with TensorFlow and/or PyTorch

Statistics & Mathematics :

- Solid foundation in :

1. Probability

2. Statistics

3. Hypothesis testing

- Statistical distributions

- Correlation and regression analysis

Model Deployment & Production :

- Experience deploying ML models into production

- Understanding of the end-to-end ML lifecycle

- Experience with REST APIs for model inference

- Working knowledge of Docker

- Exposure to cloud platforms such as AWS / GCP / Azure

Education :

Bachelors or Masters degree in Computer Science, Data Science, Statistics, Mathematics, or Engineering