Machine Learning Engineer

Machine Learning Engineer :

Key Responsibilities:

Design and implement machine learning workflows from data ingestion to model deployment.

Develop, train, and fine-tune supervised and unsupervised ML models for business applications.

Translate complex business problems into ML-based solutions and measurable outcomes.

Build and automate model training, evaluation, and deployment pipelines.

Collaborate with cross-functional teams to understand requirements and deliver production-grade models.

Ensure scalability, reliability, and performance of deployed models.

Monitor model accuracy, drift, and performance, and manage continuous improvement cycles.

Document ML experiments, workflows, and results for reproducibility.

Required Skills & Qualifications:

Bachelors or Masters degree in Computer Science, Artificial Intelligence, or related field.

Strong hands-on experience in building and deploying ML models end-to-end.

Expertise in machine learning algorithms, model evaluation, and workflow orchestration.

Experience with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch.

Good understanding of MLOps practices, including CI/CD pipelines and model versioning.

Experience in deploying ML solutions using APIs, containers, or cloud platforms (AWS, Azure, or GCP).

Proven experience delivering real-world ML use cases such as classification, regression, forecasting, or recommendation systems.

Good to Have:

Exposure to automation frameworks or ML pipeline tools (MLflow, Airflow, Kubeflow, etc.).

Familiarity with data versioning and monitoring tools.

Understanding of data governance and model lifecycle management.

Soft Skills:

Strong problem-solving and analytical skills.

Excellent communication and documentation abilities.

Ability to work independently and within collaborative teams.