Position Summary:

The Machine Learning Engineer is responsible for designing, building, deploying, and optimizing machine learning models and data-driven solutions that support business objectives. This role bridges software engineering and data science, ensuring ML models are scalable, efficient, production-ready, and integrated seamlessly into applications and systems. The ML Engineer collaborates with data scientists, software developers, product managers, and domain experts to turn analytical prototypes into robust, high-performing ML pipelines.

A Snapshot of your Day

How You’ll Make an Impact (responsibilities of role)

Model Development & Optimization

• Develop, implement, and optimize machine learning models for classification, regression, forecasting, and other analytics tasks.
• Collaborate with data scientists to refine features, algorithms, and model architecture.
• Evaluate model performance using appropriate metrics and ensure continuous improvement.

2. ML Pipeline Engineering

• Build scalable, automated ML pipelines (data preprocessing, training, validation, deployment).
• Implement MLOps best practices, including CI/CD for ML, versioning, monitoring, and retraining workflows.
• Use modern ML frameworks such as TensorFlow, PyTorch, Scikit-learn, or XGBoost.

3. Data Engineering & Preparation

• Work with large, structured and unstructured datasets.
• Build data processing workflows using Python, SQL, and big data tools.
• Ensure data quality, consistency, and proper feature engineering.

4. Deployment & Productionization

• Deploy machine learning models to cloud or on-premise environments (AWS, Azure, GCP).
• Develop REST APIs, microservices, or batch processes to expose ML capabilities.
• Monitor model performance in production and address drift, degradation, or bias issues.

5. Collaboration & Documentation

• Work closely with software engineers to integrate ML systems into applications.
• Partner with data scientists to validate model assumptions and ensure reproducibility.
• Document ML workflows, architecture, and operational procedures.

6. Research & Innovation

• Stay updated with new ML techniques, tools, and best practices.
• Evaluate new technologies (e.g., LLMs, transformers, AutoML, graph ML).
• Experiment with advanced models and help drive innovation within the organization.

What You Bring (required qualification and skill sets)

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, Engineering, or related field.
• 2–5+ years of experience developing and deploying machine learning solutions.
• Strong experience with Python and ML libraries (TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy).
• Experience with cloud ML services (AWS SageMaker, Azure ML, GCP Vertex AI).
• Solid understanding of mathematics and statistics related to ML (optimization, probability, linear algebra).
• Knowledge of data engineering tools (Spark, Kafka, Airflow, Databricks) is a plus.
• Experience building APIs and production services (FastAPI, Flask, Django).
• Strong problem-solving and analytical thinking.
• Curiosity about new technologies and a growth mindset.
• Ability to balance experimentation with scalability and reliability.
• Effective communication and teamwork skills.
• Attention to detail and strong ownership of deliverables.

Preferred Qualifications

• Experience with deep learning, NLP, computer vision, or time-series forecasting.
• Familiarity with MLOps platforms (MLflow, Kubeflow, DVC, BentoML).
• Knowledge of containerization and orchestration (Docker, Kubernetes).
• Background in distributed computing or big data processing.
• Experience applying ML in domain-specific environments (IoT analytics, finance, geospatial, or industrial systems).