Role Summary

This role focuses on building production-ready ML systems by collaborating closely with data scientists, data engineers, and software development teams. You will utilize both structured and unstructured data to create impactful ML solutions, requiring strong expertise in model development, experimentation, optimization, and deployment.

Key Responsibilities

Model Development & Experimentation

• Design, develop, and implement machine learning models for classification, regression, clustering, NLP, computer vision, or recommendation systems.
• Build and train ML models using large-scale structured and unstructured datasets.
• Perform feature engineering, data cleaning, and preprocessing to improve model accuracy.
• Conduct exploratory data analysis (EDA) to identify trends and patterns.
• Select appropriate algorithms and fine-tune hyperparameters to optimize performance.

Model Optimization & Performance

• Improve model performance through validation techniques and cross-validation strategies.
• Monitor model drift and retrain models when necessary.
• Optimize models for scalability, latency, and efficiency in production environments.
• Perform A/B testing and performance benchmarking.

Deployment & Productization

• Deploy ML models into production using APIs or batch/real-time pipelines.
• Collaborate with DevOps teams to implement CI/CD for ML workflows.
• Build and maintain ML pipelines and automated retraining systems.
• Ensure model reliability, monitoring, and logging in production.

Collaboration & Documentation

• Work closely with data scientists, software engineers, and product managers to translate business requirements into ML solutions.
• Document model design, experiments, and deployment processes.
• Participate in code reviews and maintain coding best practices.

Required Qualifications

• Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or related field.
• 2–6 years of hands-on experience in building and deploying machine learning models.
• Strong understanding of statistics, probability, and linear algebra.
• Experience working in Agile/Scrum development environments.

Technical Skills

Programming & Tools

• Strong proficiency in Python (mandatory); knowledge of R is a plus.
• Experience with ML libraries such as TensorFlow, Py Torch, Scikit-learn.
• Experience with data manipulation tools like Pandas and NumPy.

Machine Learning & AI

• Solid understanding of supervised and unsupervised learning techniques.
• Experience with deep learning architectures (CNNs, RNNs, Transformers).
• Knowledge of NLP or Computer Vision techniques (preferred based on domain).
• Strong expertise in feature engineering and model evaluation metrics.

Data & Infrastructure

• Experience working with SQL/NoSQL databases.
• Familiarity with big data technologies (Spark, Hadoop) is a plus.
• Experience with cloud platforms (AWS, Azure, or GCP).
• Knowledge of containerization tools like Docker and orchestration tools like Kubernetes.

Deployment & ML Ops

• Experience deploying models via REST APIs or microservices.
• Familiarity with CI/CD pipelines and ML Ops practices.
• Experience with model monitoring tools and versioning systems (ML flow, DVC, etc.).

Preferred Qualifications

• Experience working on production-grade ML systems.
• Exposure to distributed training and large-scale datasets.
• Knowledge of data security and model governance best practices.
• Experience mentoring junior engineers (for candidates closer to 6 years of experience).

Key Competencies

• Strong analytical and problem-solving skills.
• Ability to translate business problems into ML solutions.
• Excellent communication and collaboration skills.
• Self-driven with a strong ownership mindset.
• Continuous learning attitude toward emerging AI technologies.