MLOps / ML Engineer | Immediate Joiner

Value Spectrum Technologies; Hyderabad; Hybrid; INR 3,500,000 - 5,000,000; Full time

Job summary

Value Spectrum Technologies is seeking an experienced MLOps/ML Engineering professional to design, build, deploy, and scale machine learning and AI solutions that drive business value.

The ideal candidate will bring strong expertise in MLOps, cloud platforms, and software engineering best practices, and will be comfortable working across the full lifecycle of ML systems from experimentation to production-grade deployment.

Qualifications

• 6 to 8 years of hands-on MLOps/ML engineering experience.
• Strong Python programming for production-grade ML pipelines.
• Experience with MLOps tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML).
• CI/CD experience with Jenkins, GitLab CI, or Azure DevOps.
• Hands-on familiarity with AWS cloud and containerization (Docker, Kubernetes).
• Knowledge of model governance, versioning, and observability.

Responsibilities

• Deploy and maintain ML models in production for reliability and scalability.
• Design scalable ML pipelines, feature engineering frameworks, and monitoring solutions.
• Collaborate with data scientists, data engineers, platform, SRE, and product stakeholders to deliver end-to-end AI/ML use cases.
• Implement MLOps best practices including CI/CD, governance, versioning, and observability.
• Automate training, validation, and deployment workflows to reduce manual effort.
• Set up monitoring, drift detection, and alerting for production models.
• Troubleshoot production ML systems and perform root-cause analysis.
• Contribute to documentation and knowledge sharing on ML infra and tooling.

Skills

Python

ML Pipelines

MLOps

CI/CD

Kubeflow

SageMaker

Vertex AI

Azure ML

Docker

Kubernetes

Observability

Tools

MLflow

Kubeflow

SageMaker

Vertex AI

Azure ML

Jenkins

GitLab CI

Azure DevOps

Docker

Kubernetes

AWS

Job description

Job Summary

We are looking for an experienced MLOps / ML Engineering professional to design, build, deploy, and scale machine

learning and AI solutions that drive business value.

The ideal candidate will bring strong expertise in MLOps, cloud platforms, and software engineering best practices, and will

be comfortable working across the full lifecycle of ML systems from experimentation to production-grade deployment.

Key Responsibilities

• Deploy and maintain machine learning models in production environments, ensuring reliability and scalability.
• Design scalable ML pipelines, feature engineering frameworks, and model monitoring solutions.
• Collaborate closely with data scientists, data engineers, platform, SRE, and product stakeholders to deliver AI/ML use cases end-to-end.
• Implement MLOps best practices, including CI/CD for ML, model governance, versioning, and observability.
• Automate model training, validation, and deployment workflows to reduce manual effort and time-to-production.
• Set up and maintain model performance monitoring, drift detection, and alerting mechanisms.
• Support troubleshooting and root-cause analysis for issues arising in production ML systems.
• Contribute to documentation and knowledge-sharing on ML infrastructure and tooling.

Required Skills & Experience

• 6 to 8 years of overall experience, with substantial hands‑on exposure to MLOps / ML engineering.
• Strong programming skills in Python, with experience building production‑grade ML pipelines.
• Hands‑on experience with MLOps tooling such as MLflow, Kubeflow, SageMaker, Vertex AI, or Azure ML.
• Experience with CI/CD pipelines and tools such as Jenkins, GitLab CI, or Azure DevOps.
• Working knowledge on cloud platform AWS
• Experience with containerization and orchestration technologies (Docker, Kubernetes).
• Familiarity with model monitoring and observability tools.
• Understanding of model governance, versioning, and compliance requirements in regulated environments.
• Strong collaboration, communication, and stakeholder‑management skills.

Good to Have

• Exposure to feature stores and data versioning tools.
• Experience in the banking / financial services domain.