AI / Machine Learning Engineer – MLOps

Umanist NA; Maharashtra; On-site; INR 350,000 - 750,000; Full time

Job summary

Umanist NA is seeking an experienced AI/ML Engineer with a strong focus on MLOps and production operations in Pune. The role involves managing ML workflows, monitoring model health, and ensuring governance across the ML lifecycle for client-facing solutions.

The candidate will collaborate with international stakeholders, support data quality, and drive continuous improvement of MLOps practices in a fast-paced telecom analytics environment.

Qualifications

• 8+ years of AI/ML, MLOps, or data engineering.
• Strong hands-on with MLOps processes and tools.
• Experience with ML platforms like MLflow, Databricks, Azure ML.

Responsibilities

• Design, implement, and manage MLOps workflows and production ops.
• Monitor stability and health of ML models and pipelines.
• Manage ML model lifecycle: registration, versioning, deployment tracking, lineage, reproducibility, monitoring, governance.
• Implement data drift and performance monitoring; incident handling and escalation.
• Support CI/CD, scripting, Git workflows, and production ops.
• Collaborate with stakeholders to resolve ML operational issues and improve processes.

Skills

MLOps

Data Engineering

SQL

Python

Cloud ML Platforms

Model Lifecycle Management

Stakeholder Management

Communication

Databricks

BI Dashboards

Education

Bachelor's or Master's in Computer Science or related field

Tools

MLflow

Databricks

Azure ML

Git

CI/CD

Job description

Location: Pune

Experience: 8-11 Years(30575)

Notice Period: Immediate to 45 Days

Employment Type: Full-Time

Interview Process: 2 Technical Rounds + 1 Client Round + HR

Role Overview

We are looking for an experienced AI / Machine Learning Engineer with a strong focus on MLOps and production operations.

The role will be responsible for managing MLOps workflows, tools, and production support processes for machine learning solutions. You will ensure the stability, reliability, performance, governance, and continuous improvement of ML models and pipelines throughout their lifecycle.

This is a client-facing role requiring excellent communication and stakeholder-management skills.

Key Responsibilities

• Design, implement, and manage MLOps workflows, tools, and operational processes for AI/ML solutions.
• Monitor the day-to-day stability, reliability, and operational health of ML models and pipelines.
• Manage the complete ML model lifecycle, including:
• Model Registration
• Versioning
• Deployment Tracking
• Lineage
• Reproducibility
• Monitoring
• Governance
• Implement monitoring for data drift, concept drift, model performance degradation, inference quality, and service-level issues.
• Develop and execute incident handling, recovery, rollback, and escalation procedures for ML-related issues.
• Support data quality, lineage tracking, and governance throughout the ML lifecycle.
• Perform SQL-based data validation, troubleshooting, monitoring, and reporting.
• Develop and maintain dashboards and alerts for ML and production environments.
• Support CI/CD, scripting, Git-based workflows, and production operations.
• Translate ML governance principles into practical implementation and operational processes.
• Collaborate with technical and non-technical stakeholders to identify and resolve complex ML operational issues.
• Contribute to continuous improvement of MLOps processes, tools, and production support practices.

Must-Have Skills

• 7+ years of relevant experience in AI/ML, MLOps, data engineering, or related areas.
• Strong hands-on experience with MLOps processes and tools.
• Hands-on experience with MLOps/cloud ML platforms such as:
• MLflow
• Databricks
• Azure ML
• Or equivalent platforms
• Strong SQL and data analysis skills.
• Experience with model lifecycle management, including:
• Model Registry
• Versioning
• Lineage
• Reproducibility
• Deployment Tracking
• Monitoring
• Experience with Databricks and ML workflows.
• Experience with Power BI / Tableau / Databricks SQL Dashboards or equivalent dashboarding and alerting tools.
• Working knowledge of Git, CI/CD, scripting, and production support practices.
• Understanding of ML model monitoring, data quality, and governance.
• Strong analytical and problem-solving skills.
• Excellent communication and stakeholder-management skills.
• Ability to explain complex technical risks and solutions to non-technical stakeholders.
• Self-driven and proactive approach to working in a fast-paced environment.
• Familiarity with ML and data development processes in a telecommunications environment.
• Bachelor's or Master's degree in Computer Science or a related field.

Good-to-Have Skills

• Experience with advanced MLOps/cloud ML platforms beyond the core stack.
• Experience implementing automated model monitoring and alerting frameworks.
• Experience with production incident management, rollback, recovery, and escalation processes.
• Knowledge of advanced ML governance and compliance practices.
• Experience with cloud-based data and ML architectures.
• Experience in telecommunications / telecom analytics.
• Experience working with large-scale enterprise ML environments.
• Relevant certifications in cloud, data, AI/ML, or MLOps.

Additional Information

• Notice Period: Immediate to 45 Days
• Location: Pune; local candidates preferred.
• The role involves regular interaction with international stakeholders and teams.
• Selected candidates may be required to travel to Singapore within 30-45 days of onboarding, subject to documentation and travel formalities.
• During the Singapore assignment, an additional SGD 3,500 per month will be provided toward accommodation and other expenses.
• Candidates should have strong employment stability, preferably 2+ years with an organization.
• Excellent communication skills are mandatory due to the client-facing nature of the role.

Skills: data analysis,telecommunications domain,databricks sql dashboards,data engineering,versioning,lineage,sql,data development processes,azure ml,ml,mlops processes,ai/ml,mlops,model registry,reproducibility,databricks,deployment tracking,mlflow,model lifecycle management,tableau,power bi