Job Description:
As a Machine Learning Engineer, you will design, build, and maintain machine learning systems that integrate seamlessly into our production environment. You’ll work closely with data scientists, researchers, and software engineers to bring ML models from prototype to scale. This is a hands-on engineering role requiring both ML expertise and strong software development skills.
You’ll be responsible for optimizing data pipelines, automating model training workflows, and ensuring robust deployment of models for performance, scalability, and reliability. The ideal candidate is passionate about deploying real-time, reliable AI solutions and has experience in applying MLOps best practices.
Responsibilities:
-
Build, test, and deploy end-to-end machine learning pipelines for training, validation, and inference.
-
Convert research prototypes into scalable, production-ready ML solutions.
-
Collaborate with data scientists and product engineers to define ML system requirements and success metrics.
-
Manage and maintain model versioning, monitoring, and performance tuning in production environments.
-
Implement best practices in MLOps, including CI/CD for ML, reproducibility, and model governance.
-
Design data preprocessing workflows and engineer robust features from structured and unstructured data.
-
Work with large-scale datasets and optimize pipelines for distributed processing (e.g., Spark, Dask).
-
Integrate models into applications using APIs, microservices, or cloud-based deployment tools.
-
Monitor deployed models to detect drift, performance degradation, or bias over time.
-
Document system architecture, model specifications, and development processes.
Preferred Qualifications:
Required
-
Bachelor’s or Master’s degree in Computer Science, Engineering, Applied Mathematics, or a related field.
-
3+ years of experience in machine learning model development and deployment.
-
Strong programming skills in Python and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
-
Experience with software engineering best practices, including testing, version control, and modular design.
-
Proficient in working with data pipelines and cloud platforms (e.g., AWS, GCP, Azure).
-
Familiar with containerization and orchestration tools like Docker and Kubernetes.
-
Solid grasp of machine learning fundamentals, model evaluation, and deployment challenges.
-
Experience using MLOps tools (e.g., MLflow, TFX, SageMaker, Vertex AI).
Preferred
-
Exposure to deep learning architectures, NLP, or time-series forecasting.
-
Experience with real-time ML systems (e.g., streaming inference, online learning).
-
Familiarity with DevOps tools and infrastructure-as-code (e.g., Terraform, Ansible).
-
Contributions to open-source ML/engineering projects.
-
Strong cross-functional collaboration and communication skills.
