Machine Learning Engineer

The ideal candidate possesses a strong blend of technical expertise and data-driven problem-solving skills. As a Machine Learning Engineer, you will play a crucial role in designing, building, and optimizing our ML pipelines and platforms while mentoring junior engineers. 

Roles & Responsibilities:  

• Collaborate with data scientists to develop, train, and evaluate machine learning models.

• Build and maintain MLOps pipelines, including data ingestion, feature engineering, model training, deployment, and monitoring.

• Leverage cloud platforms (AWS, GCP, Azure) for ML model development, training, and deployment.

• Implement best practices to automate ML workflows and improve efficiency.

• Conduct A/B testing and experimentation to optimize model performance.

• Work closely with data scientists, engineers, and product teams to deliver ML solutions.

• Contribute to the design, development, and implementation of data pipelines, ETL/ELT processes, and data integration solutions. 

• Take ownership of ML pipeline projects from inception to deployment, managing scope, timelines, and risks. 

• Ensure data quality and integrity through rigorous testing and monitoring. 

• Identify and resolve complex data-related challenges. 

• Adhere to data engineering best practices and standards. 

• Experience developing in an Agile development environment, and comfortable with Agile terminology and ceremonies. 

• Familiarity with code versioning using GIT, Jenkins and code migration tools. 

• Identifying and implementing opportunities for automation and CI/CD. 

• Stay updated with the latest trends and advancements

Functional Skills: 

Must-Have Skills (Not more than 3 to 4): 

• Strong foundation in machine learning algorithms and techniques

• Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow); Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD)

• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn)

• Outstanding analytical and problem-solving skills; Ability to learn quickly; Excellent communication and interpersonal skills

• Experience with data engineering and pipeline development

Good-to-Have Skills: 

• Experience in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification

• Knowledge of NLP techniques for text analysis and sentiment analysis

• Experience in analyzing time-series data for forecasting and trend analysis

• Experience with AWS, Azure, or Google Cloud

• Experience with Databricks platform for data analytics and MLOps

Professional Certifications (please mention if the certification is preferred or mandatory for the role): 

• Any AWS Developer Certification(preferred) 

• Any Python and ML certification (preferred)

• Databricks Certification (preferred) 

• Any SAFe Agile certification (preferred) 

Soft Skills: 

• Initiative to explore alternate technology and approaches to solving problems. 

• Skilled in breaking down problems, documenting problem statements, and estimating efforts. 

• Effective communication and interpersonal skills to collaborate with cross-functional teams. 

• Excellent analytical and troubleshooting skills. 

• Strong verbal and written communication skills 

• Ability to work effectively with global, virtual teams 

• High degree of initiative and self-motivation. 

• Ability to manage multiple priorities successfully.  

• Team-oriented, with a focus on achieving team goals 

• Strong presentation and public speaking skills. 

Basic Qualifications: 

• Master’s degree in computer science or STEM majors with a minimum of 5 years of Information Systems experience OR  

• Bachelor’s degree in computer science or STEM majors with a minimum of 7 years of Information Systems experience.