Senior Backend & MLOps Engineer

Patch Infotech Pvt Ltd; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Patch Infotech Pvt Ltd in Bengaluru seeks an experienced Senior Backend and MLOps Engineer to bridge AI/ML engineering with scalable backend architecture. You will build high-performance Python microservices and architect end-to-end ML pipelines using Kubeflow and cloud-native infra.

You will collaborate with Data Scientists and DevOps to productionize, monitor, and scale AI models in production, implementing robust APIs and serving strategies at scale.

Qualifications

• Strong Python backend development experience with asynchronous programming patterns.
• Hands-on experience building and maintaining production-grade APIs for AI/ML workloads.
• Experience designing scalable data processing pipelines and database optimisation.
• Proficiency with Kubeflow Pipelines and Kubeflow Notebooks in production.
• Experience with container orchestration (Kubernetes) and cloud-native infra.

Responsibilities

• Design, build, and maintain high-throughput microservices (FastAPI/Flask/Django).
• Architect production-grade APIs for AI model inference and data processing pipelines.
• Optimise databases (PostgreSQL/Redis/MongoDB) and vector stores.
• Implement asynchronous task queues (Celery/RabbitMQ/Kafka).
• Architect and manage ML pipelines using Kubeflow; automate CI/CD for ML workflows.
• Standardise model serving using KServe/Triton/BentoML and manage experiments.

Skills

Python

Asyncio

API design

ML model deployment

Data pipelines

Tools

Kubeflow

Kubernetes

Docker

CI/CD (ArgoCD/GitHub Actions)

MLflow/Feast

Job description

We are seeking an experienced Senior Backend and MLOps Engineer to bridge the gap between AI/ML engineering, scalable backend architecture, and production machine learning infrastructure. In this role, you will build and maintain high-performance Python microservices while architecting end-to-end ML pipelines using Kubeflow and cloud-native infrastructure. You will work closely with Data Scientists and DevOps teams to productionize, monitor, and scale AI models in production.

Backend Engineering (Python)

The candidate will have responsibilities across the following functions:

• Design, build, and maintain high-throughput, low-latency microservices using FastAPI, Flask, or Django.
• Architect production-grade APIs for AI model inference and data processing pipelines.
• Optimise database performance (PostgreSQL, Redis, MongoDB, vector databases like Pinecone/Weaviate/Milvus).
• Implement robust asynchronous task queues using Celery, RabbitMQ, or Kafka.

MLOps And Orchestration (Kubeflow)

• Architect, deploy, and manage production ML pipelines using Kubeflow Pipelines (KFP) and Kubeflow Notebooks.
• Implement automated CI/CD for machine learning (CT/CD) including automated retrain triggers, model evaluation, and deployment.
• Standardise model serving using frameworks such as KServe, Triton Inference Server, or BentoML.
• Manage model versioning, feature stores, and experiment tracking using tools like MLflow, Feast, or Weights & Biases.

Infrastructure And Cloud

• Work heavily with Kubernetes (K8S), Helm, and Docker to deploy and scale AI workload clusters.
• Manage cloud-native AI infrastructure across AWS, GCP, or Azure (EKS/GKE/AKS).
• Ensure high availability, security, and cost-efficiency for GPU/CPU workloads.
• Implement robust monitoring, logging, and alerting for model drift, latency, and system health using Prometheus, Grafana, and the ELK stack.

Requirements

• Experience: 5+ years of hands-on experience in software engineering, backend development, and MLOps.
• Core Language: Advanced proficiency in Python (asyncio, memory management, multiprocessing, object-oriented design).
• MLOps Core: Deep hands-on experience with Kubeflow (Pipelines, KServe, Katib) in a production environment.
• Containerization and Orchestration: Strong expertise in Docker and Kubernetes (CRDs, ingress controllers, resource limits, GPU node pools).
• ML Ecosystem: Practical knowledge of ML frameworks (PyTorch, TensorFlow, Scikit-learn) and LLM deployment frameworks (vLLM, Ollama, Hugging Face ecosystem).
• Databases and Queues: Experience with SQL/NoSQL databases, Vector DBs, and event-driven architectures (Kafka/RabbitMQ/Redis).
• CI/CD: Experience setting up GitOps pipelines (ArgoCD, GitHub Actions, GitLab CI/CD) tailored for ML workflows.