We are fueled by a moral imperative to advance mankind, and it all begins with our people, our product, and our purpose. Passion isn’t something we turn on and off; it’s woven into everything we do. If you thrive in high-challenge environments, are inspired by exceptional teammates, and are driven to grow beyond what you thought possible, MX is where you belong.
Come build the future with us. Join an award-winning company that isn’t just shaping the financial industry, but transforming it in ways that create meaningful, lasting impact for millions of people.
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 ResponsibilitiesArchitect 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.
Experience: 5–8 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.
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.
Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related technical field.
At MX, we are unblocking the future of finance. You will be part of a co-located team in Chennai, working on high-visibility projects that directly impact our ability to deliver real-time financial insights. If you enjoy the challenge of managing complex Kubernetes clusters and cutting-edge Vertex AI implementations, we want to hear from you.
Compensation
The expected earnings for this role could be comprised of a base salary and other forms of cash compensation, such as bonus or commissions as applicable.
This pay range is just one component of MX’s total rewards package. MX takes a number of factors into account when determining individual starting pay, including job and level they are hired into, location, skillset, peer compensation.
**Please note applicants applying for this position must have the legal right to work in India without the need for sponsorship. We are unable to provide work sponsorship for this role, and candidates should be able to verify their eligibility to work in the country independently. Proof of eligibility to work in India will be required as part of the hiring process.
Work Environment
In this role, a significant aspect of the job involves working in the office for a standard 40-hour workweek. We believe that the collaborative nature of our work and the face-to-face interactions among team members are essential for fostering a dynamic and productive work environment. Being present in the office enables seamless communication, facilitates quick decision-making, and encourages spontaneous collaboration that contributes to the overall success of our projects. We value the synergy that comes from having our team members physically together, allowing for immediate problem-solving, idea exchange, and team building.
