Senior Technical Specialist

Job Summary

Key Responsibilities

• Design and automate end-to-end ML pipelines (CI/CD, CT) for training, evaluation, and deployment.
• Productionize ML models into clean, modular, and maintainable codebases.
• Implement versioning and governance practices for data, code, and models.
• Monitor model performance, data drift, and system reliability.
• Collaborate with cross-functional teams to deliver scalable ML solutions.

Must-Have Skills

• Minimum 6 years of experience in ML Engineering or MLOps.
• Strong proficiency in Python and SQL.
• Experience with Apache Spark or similar frameworks for large-scale data processing.
• Hands-on experience with CI/CD tools (e.g., Azure DevOps).
• Familiarity with MLOps frameworks, model registries, and experiment tracking.
• Proficient in Git for version control.

Nice-to-Have Skills

• Experience with Docker, Kubernetes, and ML monitoring tools.
• Solid understanding of ML algorithms (e.g., regression, classification, clustering).
• Familiarity with libraries such as scikit-learn and pandas.
• Ability to translate business requirements into technical solutions.

Soft Skills

• Strong customer focus and stakeholder engagement.
• Ability to align technical deliverables with business objectives.

Key Responsibilities

Key Responsibilities

• Design and automate end-to-end ML pipelines (CI/CD, CT) for training, evaluation, and deployment.
• Productionize ML models into clean, modular, and maintainable codebases.
• Implement versioning and governance practices for data, code, and models.
• Monitor model performance, data drift, and system reliability.
• Collaborate with cross-functional teams to deliver scalable ML solutions.

Must-Have Skills

• Minimum 6 years of experience in ML Engineering or MLOps.
• Strong proficiency in Python and SQL.
• Experience with Apache Spark or similar frameworks for large-scale data processing.
• Hands-on experience with CI/CD tools (e.g., Azure DevOps).
• Familiarity with MLOps frameworks, model registries, and experiment tracking.
• Proficient in Git for version control.

Nice-to-Have Skills

• Experience with Docker, Kubernetes, and ML monitoring tools.
• Solid understanding of ML algorithms (e.g., regression, classification, clustering).
• Familiarity with libraries such as scikit-learn and pandas.
• Ability to translate business requirements into technical solutions.

Soft Skills

• Strong customer focus and stakeholder engagement.
• Ability to align technical deliverables with business objectives.

Skill Requirements

Key Responsibilities

• Design and automate end-to-end ML pipelines (CI/CD, CT) for training, evaluation, and deployment.
• Productionize ML models into clean, modular, and maintainable codebases.
• Implement versioning and governance practices for data, code, and models.
• Monitor model performance, data drift, and system reliability.
• Collaborate with cross-functional teams to deliver scalable ML solutions.

Must-Have Skills

• Minimum 6 years of experience in ML Engineering or MLOps.
• Strong proficiency in Python and SQL.
• Experience with Apache Spark or similar frameworks for large-scale data processing.
• Hands-on experience with CI/CD tools (e.g., Azure DevOps).
• Familiarity with MLOps frameworks, model registries, and experiment tracking.
• Proficient in Git for version control.

Nice-to-Have Skills

• Experience with Docker, Kubernetes, and ML monitoring tools.
• Solid understanding of ML algorithms (e.g., regression, classification, clustering).
• Familiarity with libraries such as scikit-learn and pandas.
• Ability to translate business requirements into technical solutions.

Soft Skills

• Strong customer focus and stakeholder engagement.
• Ability to align technical deliverables with business objectives.

Other Requirements

Key Responsibilities

• Design and automate end-to-end ML pipelines (CI/CD, CT) for training, evaluation, and deployment.
• Productionize ML models into clean, modular, and maintainable codebases.
• Implement versioning and governance practices for data, code, and models.
• Monitor model performance, data drift, and system reliability.
• Collaborate with cross-functional teams to deliver scalable ML solutions.

Must-Have Skills

• Minimum 6 years of experience in ML Engineering or MLOps.
• Strong proficiency in Python and SQL.
• Experience with Apache Spark or similar frameworks for large-scale data processing.
• Hands-on experience with CI/CD tools (e.g., Azure DevOps).
• Familiarity with MLOps frameworks, model registries, and experiment tracking.
• Proficient in Git for version control.

Nice-to-Have Skills

• Experience with Docker, Kubernetes, and ML monitoring tools.
• Solid understanding of ML algorithms (e.g., regression, classification, clustering).
• Familiarity with libraries such as scikit-learn and pandas.
• Ability to translate business requirements into technical solutions.

Soft Skills

• Strong customer focus and stakeholder engagement.
• Ability to align technical deliverables with business objectives.