Machine Learning Engineer

Rhyom; India; Hybrid; INR 2,500,000 - 4,000,000; Full time

Job summary

Rhyom is seeking a Machine Learning Engineer to co-design, build, and operate an on‑prem ML platform from scratch. You will develop models, create robust ML pipelines, and own production deployment on Kubernetes, shaping the platform beyond model development.

You will implement end-to-end pipelines, manage datasets and artifacts with MinIO, and integrate with PostgreSQL/Redis. The role emphasizes reproducibility, experiment tracking, and GPU‑based workloads within a GitOps/CI‑CD mindset.

Qualifications

• Strong experience building and deploying production ML systems.
• Hands-on with on-prem ML platforms and GPU workloads.
• Experience configuring workloads with Kubernetes and Docker for production.
• Familiar with ML lifecycle management and reproducibility.

Responsibilities

• Design and implement end-to-end ML pipelines on an on‑prem platform.
• Deploy and maintain model serving on Kubernetes (real-time & batch).
• Own MLOps workflows including training automation, promotion, rollback, and retraining.
• Instrument workloads with monitoring/logging and ensure security using RBAC.

Skills

ML pipelines

Python

PyTorch

TensorFlow

Kubernetes

Docker

MLOps

GPU workloads

PostgreSQL

MinIO

Redis

Experiment tracking

GitOps

Tools

Docker

Kubernetes

PostgreSQL

Redis

MinIO

Job description

Role Overview

We are hiring a Machine Learning Engineer who will co-own the design, implementation, and operation of a fully on-premises ML platform built from scratch. In addition to developing models, you will be responsible for MLOps, infrastructure-aware ML pipelines, and production deployment on Kubernetes. This role goes beyond modeling you will help build the platform itself.

Key Responsibilities

• Design and implement end-to-end ML pipelines (data processing, training, evaluation, deployment) using Kubernetes Jobs.
• Build containerized ML workloads using Docker and define runtime environments.
• Deploy and maintain model serving services on Kubernetes (real-time and batch inference).
• Implement experiment tracking, model versioning, and reproducibility from scratch.
• Store and manage datasets, features, and model artifacts in MinIO.
• Integrate ML workflows with PostgreSQL for metadata, pipeline state, and model registry.
• Own MLOps workflows: training automation, model promotion, rollback, and re-training.
• Instrument ML workloads with Datadog for metrics, logs, traces, and alerts.
• Configure and secure ML workloads using Kubernetes ConfigMaps, Secrets, and RBAC.
• Optimize pipelines for performance, cost, and GPU usage.
• Collaborate closely with the Backend Engineer to define APIs, schemas, and system boundaries.
• Participate in infrastructure decisions, CI/CD setup, and production operations.

Required Qualifications

• Strong experience building and deploying production ML systems.
• Advanced proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Hands-on experience with Docker and Kubernetes (not just using them, but configuring workloads).
• Experience with on-prem ML platforms.
• Familiarity with MinIO, PostgreSQL, Redis.
• Experience running ML workloads on GPUs.
• Exposure to CI/CD tooling and GitOps-style workflows.
• Prior experience as an MLOps or hybrid ML/Infra engineer.
• Experience building ML pipelines without managed cloud services.
• Understanding of experiment tracking, model lifecycle management, and reproducibility.
• Comfortable owning systems in production (debugging, monitoring, on-call mindset).