ML OPS Engineer

Bangalore

About Us

We empower enterprises globally through intelligent, creative, and insightful services for data integration, data analytics and data visualization.

Hoonartek is a leader in enterprise transformation, data engineering and an acknowledged world-class Ab Initio delivery partner.

Using centuries of cumulative experience, research and leadership, we help our clients eliminate the complexities & risk of legacy modernization and safely deliver big data hubs, operational data integration, business intelligence, risk & compliance solutions and traditional data warehouses & marts.

At Hoonartek, we work to ensure that our customers, partners and employees all benefit from our unstinting commitment to delivery, quality and value. Hoonartek is increasingly the choice for customers seeking a trusted partner of vision, value and integrity

How We Work?

Define, Design and Deliver (D3) is our in-house delivery philosophy. It's culled from agile and rapid methodologies and focused on 'just enough design'. We embrace this philosophy in everything we do, leading to numerous client success stories and indeed to our own success.

We embrace change, empowering and trusting our people and building long and valuable relationships with our employees, our customers and our partners. We work flexibly, even adopting traditional/waterfall methods where circumstances demand it. At Hoonartek, the focus is always on delivery and value.

Job Description

Job Title: MLOps Engineer

Experience: 2+ Years

Department: Data Science / AI Platform / Analytics Engineering

Role Summary

We are looking for an experienced MLOps Engineer to design, implement, and maintain scalable machine learning pipelines and production systems. The ideal candidate will bridge the gap between data science, engineering, and operations teams to enable reliable, automated, and compliant ML model deployment and monitoring.

________________________________________

Key Responsibilities

Model Lifecycle Management

• Automate end-to-end ML workflows — from data preparation, training, and evaluation to deployment and retraining.
• Work with Data Scientists to productionize ML models (batch and real-time).
• Manage model versioning, lineage, and reproducibility using tools like MLflow, Vertex AI, or Kubeflow.
• Implement model performance monitoring (data drift, model drift, bias, latency, etc.).

Infrastructure & Automation

• Build and maintain CI/CD pipelines for ML projects using GitHub Actions, Jenkins, or Cloud Build.
• Containerize ML applications using Docker and deploy on Kubernetes / GKE / EKS.
• Implement infrastructure as code (IaC) using Terraform or Cloud Deployment Manager.
• Optimize compute and storage costs across environments (dev, test, prod).

Data & Feature Engineering Integration

• Collaborate with Data Engineering teams to integrate with feature stores (Feast, Vertex AI Feature Store, etc.).
• Ensure data validation, schema checks, and pipeline observability (using Great Expectations, TFDV, etc.).

Governance, Security & Compliance

• Manage access control, service accounts, and IAM roles for ML pipelines.
• Ensure model governance, auditability, and compliance (especially for regulated domains like BFSI).
• Track experiments, metadata, and ensure traceability of data and model artifacts.

Monitoring & Observability

• Set up dashboards and alerts (e.g., using Prometheus, Grafana, EvidentlyAI).
• Automate model retraining triggers based on data drift or performance thresholds.

________________________________________

Required Skills

Category Skills

Languages Python, SQL, Bash

ML Frameworks TensorFlow, PyTorch, Scikit-learn

MLOps Tools MLflow, Vertex AI

Cloud Platforms GCP (preferred), or AWS, or Azure

CI/CD & Infra GitHub Actions, Docker, Kubernetes

Data Tools BigQuery, Dataflow

________________________________________

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, or related field.
• 2+ years of experience in ML, Data Engineering, or DevOps / MLOps roles.
• Strong understanding of ML lifecycle management, data pipelines, and model deployment patterns.
• Experience with GCP Vertex AI, AI Platform Pipelines preferred.

Job Requirement

Category Skills

Languages Python, SQL, Bash

ML Frameworks TensorFlow, PyTorch, Scikit-learn

MLOps Tools MLflow, Vertex AI

Cloud Platforms GCP (preferred), or AWS, or Azure

CI/CD & Infra GitHub Actions, Docker, Kubernetes

Data Tools BigQuery, Dataflow