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.
- Solid 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.

Must Have Skill

Python,CI/CD,MLOps (Machine Learning Operations),Machine Learning Algorithms

Good to Have Skill

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.

📌 Senior Technical Specialist (Bengaluru)
🏢 HCLTech
📍 Bengaluru