MLOps + Full Stack

• MLOps + FullStack :
• Contract to Hire
• Experience : 6 -12 Years
• Location : PAN INDIA

Job Description: Senior MLOps & Full Stack Systems Engineer

Job Summary

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.