Machine Learning Engineer

Role Summary:

We are seeking a results-driven and innovative Machine Learning Engineer with 26 years of experience in developing, deploying, and optimizing machine learning models in production environments. The ideal candidate will have strong programming skills, hands-on experience with ML frameworks, and the ability to transform complex data into actionable insights.

You will work closely with data scientists, data engineers, and software development teams to build scalable, efficient, and reliable ML-powered systems that support business objectives.

Key Responsibilities:

Model Development & Implementation

• Design, develop, and implement machine learning models for real-world business problems.
• Work with structured and unstructured datasets including text, images, and large-scale tabular data.
• Perform exploratory data analysis (EDA) to understand data patterns and anomalies.
• Apply appropriate machine learning algorithms (supervised, unsupervised, and deep learning).
• Conduct feature engineering and data preprocessing to improve model accuracy.

Model Optimization & Evaluation

• Fine-tune models using hyperparameter optimization techniques.
• Evaluate models using appropriate performance metrics (accuracy, precision, recall, F1-score, AUC, etc.).
• Perform cross-validation and model comparison.
• Optimize models for scalability, latency, and production performance.

Deployment & Product ionization

• Deploy machine learning models into production using APIs or batch processing systems.
• Build automated training and retraining pipelines.
• Monitor model performance and handle model drift.
• Collaborate with DevOps teams to integrate ML solutions into CI/CD pipelines.

Collaboration & Communication

• Work closely with data scientists to transition prototypes into production-ready systems.
• Collaborate with software engineers to integrate ML models into applications.
• Translate business requirements into technical ML solutions.
• Document model architecture, workflows, and deployment procedures.

Required Qualifications:

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, or a related field.
• 26 years of hands-on experience in machine learning model development and deployment.
• Strong foundation in statistics, probability, linear algebra, and algorithms.
• Experience working in Agile/Scrum environments.

Technical Skills:

Programming Languages

• Strong proficiency in Python (mandatory).
• Knowledge of R is a plus.
• Experience with data manipulation libraries such as Pandas and NumPy.

Machine Learning Frameworks

• Hands-on experience with TensorFlow, PyTorch, and Scikit-learn.
• Experience building and training deep learning models (CNNs, RNNs, Transformers preferred).

Data Engineering & Processing

• Strong experience in data preprocessing, feature engineering, and data cleaning.
• Experience working with SQL/NoSQL databases.
• Familiarity with big data tools such as Spark is a plus.

Model Deployment & ML Ops

• Experience deploying models using REST APIs or microservices architecture.
• Familiarity with Docker and containerization tools.
• Experience with ML lifecycle tools such as Moldflow or similar.
• Knowledge of cloud platforms (AWS, Azure, or GCP) is preferred.

Preferred Qualifications:

• Experience working on large-scale or enterprise ML systems.
• Exposure to NLP, Computer Vision, Recommendation Systems, or Generative AI projects.
• Knowledge of model explainability and responsible AI practices.
• Experience mentoring junior engineers (for candidates closer to 6 years).

Key Competencies:

• Strong analytical and problem-solving skills.
• Ability to translate complex data challenges into scalable ML solutions.
• Excellent communication and collaboration skills.
• Ownership mindset with attention to detail.
• Passion for continuous learning and staying updated with emerging AI trends.