Senior Technical Specialist
India
Job Description
Senior Technical Specialist
Bangalore, Karnataka
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.
Information at a Glance