MLOps Engineer- python,react,kubernets

Cloudxtreme; Hyderabad, Bengaluru, New Delhi; Hybrid; INR 3,000,000 - 5,400,000; Full time

Job summary

Cloudxtreme is seeking a Senior MLOps & Full Stack Systems Engineer to bridge production ML infrastructure with user-facing enterprise platforms. The role blends ML pipeline delivery with full-stack interfaces (React frontend + Java/Python backend) for operationalization, monitoring, and interaction with AI models.

The ideal candidate will design end-to-end pipelines, build low-latency inference services, and develop dashboards and admin portals using React/TypeScript and Java/Python backends in

Qualifications

• Experience designing end-to-end ML delivery pipelines.
• Proficiency in Java/Python backend and React frontend.
• Experience with containerized microservices and cloud platforms.

Responsibilities

• MLOps & Platform Architecture: design, build, and maintain ML lifecycles with CI/CD/CT.
• Backend & Systems Engineering: build high-throughput microservices with Java/Python.
• Frontend & UI Engineering: develop React/TypeScript dashboards and admin portals.
• Infrastructure & Cloud: deploy on Kubernetes with IaC (Terraform/CloudFormation).
• Quality & Collaboration: enforce tests, document architecture, mentor teammates.

Skills

MLOps

CI/CD

Python

Java

React

TypeScript

Kubernetes

Docker

REST APIs

gRPC

Kafka

Terraform

Cloud (AWS/GCP/Azure)

Monitoring

Model Serving

Data Pipelines

Tools

MLflow

Kubeflow

Feast

Weights & Biases

Triton Inference Server

TorchServe

TF Serving

FastAPI

Flask

Redis

Grafana

Prometheus

Job description

Role & responsibilities

We are seeking a versatile and high-impact Senior MLOps & Full Stack Systems Engineer to bridge the gap between production machine learning infrastructure and user-facing enterprise platforms. In this hybrid engineering role, you will be responsible for designing, building, and operating end-to-end ML delivery pipelines while engineering the full-stack software interfaces (React frontend + Java/Python backend services) required to operationalize, monitor, and interact with production AI/ML models. The ideal candidate possesses a unique blend of core software engineering rigor and machine learning platform operations. You will build automated CI/CD and Continuous Training (CT) pipelines, manage model registries and feature stores, deploy low-latency model inference microservices, and build full-stack interactive dashboards and admin portals using React (TypeScript) and Java (Spring Boot) / Python (FastAPI/Flask).

Key Roles & Responsibilities

1. MLOps & Platform Architecture Design, build, and maintain automated ML lifecycle pipelines spanning data ingestion, preprocessing, model training, validation, packaging, and continuous deployment (CI/CD/CT). Implement and manage Model Registries, Experiment Tracking, and Feature Stores using tools such as MLflow, Kubeflow, Feast, or Weights & Biases. Package, optimize, and deploy ML models into production as containerized microservices (using Triton Inference Server, TorchServe, TF Serving, or FastAPI). Implement real-time model monitoring, drift detection (data drift, concept drift), latency tracking, and automated retraining workflows using tools like Evidently AI, Prometheus, and Grafana.
2. Backend & Systems Engineering (Java / Python) Architect and implement robust, high-throughput microservices using Java (Spring Boot / Micronaut) or Python (FastAPI / Flask / AsyncIO) to serve as orchestration layers between frontend applications and ML inference engines. Design and implement secure RESTful APIs, gRPC services, and event-driven data streaming pipelines using Apache Kafka, RabbitMQ, or AWS Kinesis. Optimize backend services for low-latency scoring, batch inference jobs, asynchronous request queuing, and distributed data caching (Redis). Manage data persistence and access patterns across relational (PostgreSQL, MySQL) and NoSQL (MongoDB, DynamoDB, Vector DBs) data stores.
3. Frontend & UI Engineering (React.js) Design and develop responsive, modern web applications, internal tools, and administrative control panels using React.js, TypeScript, and modern UI libraries (Tailwind CSS, MUI). Build interactive model governance dashboards, telemetry visualizations, data labeling portals, and human-in-the-loop (HITL) review interfaces. Implement state management using Redux Toolkit, React Context, or Zustand, and handle asynchronous data fetching/caching using TanStack Query (React Query). Integrate complex data visualization libraries (e.g., D3.js, Chart.js, Recharts) to render live inference metrics, feature importance plots, and operational telemetry.
4. Infrastructure, Cloud & Container Orchestration Deploy and orchestrate containerized workloads and ML pipelines on Kubernetes (EKS / GKE / AKS) using Docker, Helm, and service meshes (Istio). Implement Infrastructure as Code (IaC) using Terraform or CloudFormation to automate environment provisioning across AWS, GCP, or Azure. Configure autoscaling policies for inference endpoints based on GPU/CPU utilization and custom queue metrics. Enforce security best practices, including RBAC, secret management (HashiCorp Vault / AWS Secrets Manager), and API gateway security (OAuth 2.0 / JWT).
5. Quality, Governance & Cross-Functional Collaboration Enforce end-to-end testing standards: Unit/Integration tests for frontend (Jest, React Testing Library), backend (JUnit, PyTest), and data/model validation (Great Expectations). Collaborate closely with Data Scientists, ML Researchers, Product Owners, and DevOps teams to translate prototype algorithms into reliable, scalable production systems. Lead architecture reviews, maintain comprehensive documentation, and mentor team members in full-stack and MLOps best practices.