Senior Data Engineer

MX; Chennai District; On-site; INR 2,400,000 - 4,200,000; Full time

Job summary

MX is seeking a Senior Data Engineer with an MLOps focus to architect the next generation of our financial intelligence platform. You will bridge raw data and actionable AI, productionizing ML solutions at scale on GCP, Vertex AI, and Kubernetes, ensuring low latency in a fintech setting.

You will design ML infrastructure, deploy models, and manage streaming data pipelines, collaborating with Data Scientists and DevOps to automate the ML lifecycle using Docker and orchestration tools.

Qualifications

• 8+ years of experience in Data Engineering or MLOps (L4 equivalent).
• Extensive hands-on experience with Google Cloud Platform (GCP).
• Proven expertise in Vertex AI suite (Pipelines, Model Registry, Feature Store, Endpoints).
• Deep knowledge of Kubernetes and Docker for production workloads.
• Expert proficiency in SQL and Python for data manipulation and SDK integration.
• Experience with large-scale real-time data; Apache Flink and event-driven architectures desirable.

Responsibilities

• Architect ML infrastructure and scalable data pipelines in the GCP ecosystem.
• Deploy, monitor, and optimize ML models as production-ready Vertex AI and Ray endpoints.
• Orchestrate and fine-tune GKE clusters to support high-throughput data processing and real-time model serving.
• Collaborate with Data Scientists to automate the ML lifecycle—from training to deployment—using Docker and modern orchestration tools.
• Build and optimize streaming pipelines (Apache Flink) and implement advanced analytics structures for fast insights.
• Develop and maintain Docker images to ensure consistent environments across the ML lifecycle.

Skills

SQL

Python

GCP

Vertex AI

Kubernetes

Docker

Apache Flink

Data Sketches

Education

Bachelor's or Master's in Computer Science / Data Science / Statistics

Tools

GKE

Vertex AI Pipelines

Job description

MX is seeking a high-caliber Senior Data Engineer with a specialized focus on MLOps to architect the next generation of our financial intelligence platform. In this role, you will be the bridge between raw data and actionable AI, building the infrastructure that powers our machine learning models. You won't just move data; you will productionize ML solutions at scale using GCP, Vertex AI, and Kubernetes, ensuring our services remain resilient and low-latency in a high-stakes fintech environment.

Key Responsibilities

• Architect ML Infrastructure: Design and maintain scalable data pipelines and MLOps workflows specifically within the Google Cloud Platform (GCP) ecosystem.
• Model Productionization: Deploy, monitor, and optimize machine learning models as production-ready Vertex AI and Ray endpoints.
• Cluster Management: Orchestrate and fine-tune Kubernetes (GKE) clusters to support high-throughput data processing and real-time model serving.
• CI/CD for ML: Collaborate with Data Scientists to automate the entire ML lifecycle—from training and evaluation to seamless deployment—using Docker and modern orchestration tools.
• Real-Time Data Engineering: Build and optimize streaming pipelines (utilizing Apache Flink) and implement advanced analytical structures like Data Sketches for high-speed probabilistic analysis.
• Environment Standardization: Develop and maintain specialized Docker images to ensure consistent, reproducible environments across the full ML development lifecycle.

Technical Requirements

• Experience: 8+ years of professional experience in Data Engineering or MLOps (L4 equivalent).
• Cloud Mastery: Extensive, hands-on experience with Google Cloud Platform (GCP) is mandatory.
• Vertex AI Deep Dive: Proven expertise in the Vertex AI suite, including Pipelines, Model Registry, Feature Store, and Endpoints.
• Containerization & Orchestration: Deep technical knowledge of Kubernetes architecture and Docker best practices for production workloads.
• Programming & Data: Expert proficiency in SQL and Python is required for complex data manipulation and SDK integration.
• Streaming & Scalability: Experience handling large-scale, real-time datasets. Familiarity with Apache Flink and event-driven architectures is highly desirable.

Professional Attributes

• Fintech Mindset: Understanding of the rigor required for financial data, including idempotency, auditability, and security.
• Collaborative Leader: A track record of working effectively across multi-disciplinary teams (Data Science, DevOps, and Product).
• Problem Solver: Ability to leverage approximate computing (Data Sketches) and other advanced techniques to solve performance bottlenecks.

Education

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related technical field.