MLOps Engineer (6-10 years experience) to sit at the intersection of Data Science and Enterprise Operations — architecting, deploying, and optimizing production-grade machine learning pipelines that drive measurable improvements in our organization's MLOps maturity. This is a high-impact, technically demanding role that blends hands-on ML model development with advanced cloud infrastructure orchestration, automation engineering, and cross-functional enablement.

Core Technology Stack

Category

Tools & Platforms

ML Platforms

GCP Vertex AI AWS SageMaker Azure ML Studio

Orchestration

Apache Airflow Kubeflow

Containerization & IaC

Docker Kubernetes Terraform

Languages

Python Bash

AI/LLM Ecosystem

LangChain RAG Frameworks Vector Databases

Roles & Responsibilities

Pipeline Architecture & Automation

- Design, build, and manage end-to-end automated ML pipelines — encompassing model training, deployment, batch and real-time inference, continuous monitoring, and automated retraining workflows
- Architect scalable, distributed MLOps infrastructures optimized for high-performance training and inference at enterprise scale

MLOps Maturity & Strategy

- Own and drive the MLOps initiative backlog, systematically advancing the automation maturity, security posture, and operational resilience
- Proactively introduce modern DevOps principles — CI/CD, GitOps, infrastructure-as-code — tailored to the unique demands of the Data Science lifecycle

Model Monitoring & Reliability

- Establish proactive drift detection frameworks — monitoring both data drift and concept/model drift — to ensure sustained model accuracy and reliability in production environments
- Define and implement model governance standards, ensuring reproducibility,



auditability, and compliance across all ML workloads

API Development & Enterprise Integration

- Develop, test, and publish secure, high-performance REST APIs that enable seamless, production-ready integration of ML models with enterprise business applications
- Ensure all API implementations meet enterprise standards for security, scalability, and observability

Required Experience & Qualifications

- Proven track record operationalizing production-grade Data Science projects from experimentation through to live deployment
- Extensive Kubernetes expertise — including advanced cluster management, scaling strategies, and workload optimization
- Mastery of industry-standard MLOps frameworks with a solid conceptual understanding of ML/AI architectures and hands-on model development experience
- Deep Python proficiency for both machine learning development and infrastructure automation tasks
- Strong cloud fluency across one or more major ecosystems: AWS, Microsoft Azure, or Google Cloud Platform (GCP)
- Hands-on experience with containerization (Docker), Infrastructure-as-Code (IaC), and scalable distributed system design
- Demonstrated experience building AI agents, Retrieval-Augmented Generation (RAG) systems, enterprise-grade AI platforms, or complex workflow automation solutions

Added Advantage: Candidates with the following will have a distinct edge:

- Hands-on experience with model quantization techniques (e.g., INT8/FP16 conversion) and hardware-level optimization for both edge and cloud deployment environments
- Deep understanding of model evaluation frameworks — including precision, recall, F1/F2-score trade-offs, AUC-ROC analysis, and production metric alignment with business objectives

📌 MLOPS Engineer (Bengaluru)
🏢 Nuvento Systems
📍 Bengaluru