MLOps Engineer

MLOps Engineer - Hyderabad Location

This role is crucial for delivering scalable, reliable, and efficient AI/ML solutions that include traditional ML models, AI Agents (using ADK), LLM-based applications, and Retrieval-Augmented Generation (RAG) systems for our business and customers. You will focus on designing and building data modeling pipelines to enable deployment, monitoring, and governance of our ML models and AI agents on Google Cloud Platform (GCP). Youll collaborate closely with data scientists and ML engineers to bridge model development and production operations, accelerating delivery of innovative and impactful solutions.

Key Responsibilities As an MLOps Engineer, you will:

• Develop and maintain CI/CD/CT pipelines for ML projects on GCP, leveraging Bitbucket, Cloud Build, Docker, Artifact Registry, and Kubernetes (GKE).
• Automate end-to-end ML workflows on GCP, from data preparation to model deployment and monitoring, using Cloud Composer (Apache Airflow).
• Collaborate with ML Engineers to deploy and manage scalable ML model services on GCP such as AI Agents (using ADK), LLM-based applications, and RAG systems, utilizing Vertex AI (Endpoints, Prediction), Cloud Run, and Cloud Functions.
• Research and implement new MLOps technologies and best practices to improve the ML lifecycle and accelerate MVP delivery.
• Develop, optimize, and troubleshoot SQL, Python/R code, Docker files, Airflow DAGs, and Vertex AI/Kubeflow pipelines.
• Implement and maintain robust model observability, monitoring, logging, and alerting on GCP (Cloud Monitoring, Cloud Logging, Cloud Debugger) to detect data/label/concept drift, anomalies, and performance degradation.
• Govern the ML model lifecycle end-to-end by establishing triggers for retraining, recalibration, or rebuilding based on degradation, while ensuring robust model versioning (Git), auditability, and reproducibility using frameworks like MLflow, Kubeflow, Vertex AI Pipelines, and model registries (e.g., Vertex AI Model Registry).
• Collaborate with data scientists, engineers, and stakeholders to define requirements, provide technical support, and align with business objectives.

Qualifications To be successful in this role, you will need to have the following qualifications:

• Bachelor's degree in Computer Science, Engineering, Statistics, Informatics, or a related quantitative field, or equivalent technical experience.
• At least 3 years of professional experience in MLOps, DevOps, data science, data engineering, or software engineering, with a strong focus on production systems.
• Experience in a health care-related field or working with health care data is preferred.
• Proficiency in Python, R, and SQL.
• Extensive experience orchestrating and automating end-to-end ML workflows on GCP (Vertex AI, Cloud Composer/Airflow, Cloud Dataflow, Dataproc, Storage, BigQuery).
• Hands-on experience deploying and managing scalable ML models and pipelines using MLflow, Kubeflow (on GKE), Vertex AI, Cloud Run, and Cloud Functions.
• Demonstrated experience monitoring and logging model performance, drift, errors, and bias with GCP tools (Cloud Monitoring, Cloud Logging, Cloud Debugger).
• Experience managing model versions, auditing changes, and ensuring reproducibility/auditability using Git (Bitbucket) and model registries (e.g., Vertex AI Model Registry).
• Familiarity with the operationalization of AI Agents (ADK), LLM-based applications, and RAG systems is a strong plus.
• Strong communication, collaboration, and proactive problem-solving skills.
• Passion for learning new technologies and staying updated with AI/ML, MLOps and cloud-native trends.