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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 25+ 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).
