Patch Infotech Pvt Ltd
Website:
patchinfotech.com
Job details:
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.
This job was posted by Ganesh Singh from Patch Infotech.
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