MLOps Engineer needed for AI talent marketplace. Design scalable ML pipelines, deploy models, manage CI/CD, and ensure reliability across cloud platforms (AWS/Azure/GCP).

About the Company

Hiringhood is an innovative AI-powered talent marketplace connecting skilled professionals with top employers across India. We leverage cutting-edge AI to streamline recruitment, offering candidates a verified profile through a coding assessment and an AI technical interview. On Match Day, candidates are matched with multiple employers based on skills, experience, notice period, and compensation expectations.

Role Overview

We are seeking an experienced MLOps Engineer to join our growing team. In this role, you will bridge the gap between machine learning model development and production deployment. You will design, implement, and maintain robust ML pipelines, ensuring models are deployed at scale with high reliability, security, and observability. This position offers the opportunity to work on challenging problems across multiple domains, from healthcare to fintech, collaborating with data scientists and engineers to bring AI solutions to life.

Key Responsibilities

• Design and manage CI/CD pipelines for machine learning model deployment, automation, and versioning.
• Build and maintain scalable infrastructure using Docker, Kubernetes, and Terraform for containerized ML workloads.
• Implement model serving and inference APIs using FastAPI (or similar frameworks) with RESTful endpoints.
• Manage data versioning and model versioning using tools like MLflow, DVC, and feature stores to ensure reproducibility.
• Monitor model performance, latency, and drift using Prometheus, Grafana, and logging stacks (e.g., ELK, OpenTelemetry).
• Collaborate with data scientists to transition models from development to production, handling scaling, security, and reliability.
• Automate infrastructure provisioning and configuration using Infrastructure as Code (Terraform) and Helm charts.
• Ensure compliance with best practices for security, auditing, and governance in ML systems.
• Optimize cloud resource usage (AWS, Azure, GCP) to balance cost and performance.
• Stay updated with the latest MLOps tools and frameworks, introducing innovations to improve efficiency.

Tech Stack

• Languages & Scripting: Python, Bash, Linux
• Version Control & CI/CD: Git, GitHub Actions, GitLab CI, Jenkins, Argo Workflows
• Containerization & Orchestration: Docker, Kubernetes, Helm
• ML & Model Management: MLflow, DVC, Kubeflow, Ray, BentoML, KServe, Seldon Core
• Cloud Platforms: AWS, Microsoft Azure, Google Cloud Platform (GCP)
• Infrastructure as Code: Terraform
• Monitoring & Observability: Prometheus, Grafana, OpenTelemetry, Logging stacks
• Data Pipelines & Streaming: Airflow, Kafka, Feature Stores
• Model Serving & APIs: FastAPI, REST APIs, SageMaker, Azure ML, Vertex AI, Databricks

B.E., B.Tech, M.Tech, MCA, M.Sc (Computer Science, Information Technology, Artificial Intelligence, Data Science) or equivalent practical experience. 2-10 years of experience in MLOps or related roles. Immediate joiner preferred.

Comprehensive compensation as per experience and skills. Opportunity to work with AI-driven talent matching platform.