Senior Machine Learning Engineer – Data Science & Analytics

Job Summary

Role Overview

Core Responsibilities

Required Skills & Qualifications

Preferred

• Design and implement scalable backend architectures for ML products and services.
• Build, deploy, and monitor production-grade ML/AI systems across the product lifecycle (data ingestion, feature engineering, model integration, inference, batch/stream processing).
• Develop and optimize streaming and batch data pipelines; maintain feature stores.
• Implement infrastructure-as-code and CI/CD for ML services.
• Ensure latency, scalability, and reliability of ML systems.
• Support cloud-native ML infrastructure on AWS and GCP.
• Collaborate cross-functionally with Data Engineering, Architecture, Governance, and Security teams.
• Contribute to system design and technical architecture decisions.

5+ years of software engineering experience with cloud-native product solutions.

Strong expertise in Python, SQL, PySpark, Docker.

Experience building and maintaining backend systems for ML/algorithmic workloads.

Strong AWS cloud experience; familiarity with GCP.

Track record in designing large-scale streaming and batch data architectures.

Experience with DevOps, CI/CD, and infrastructure-as-code.

Strong analytical, collaboration, and communication skills.

Ability to operate in ambiguous, fast-paced environments and drive end-to-end solutions.

Preferred:

• Experience with SageMaker, feature stores, real-time inference, hospitality or personalization systems, and LLM-enabled apps.

Bachelor's or Master's degree in Computer Science, Software Engineering, or related field (or equivalent experience).