Principal ML Systems Engineer

About RateGain

RateGain Travel Technologies Limited is a global provider of AI-powered SaaS solutions for travel and hospitality that works with 3,200+ customers and 700+ partners in 100+ countries helping them accelerate revenue generation through acquisition, retention, and wallet share expansion. RateGain today is one of the world’s largest processors of electronic transactions, price points, and travel intent data helping revenue management, distribution and marketing teams across hotels, airlines, meta-search companies, package providers, car rentals, travel management companies, cruises and ferries drive better outcomes for their business.

Founded in 2004 and headquartered in India, today RateGain works with 26 of the Top 30 Hotel Chains, 25 of the Top 30 Online Travel Agents, 4 of the Top 5 Airlines, and all the top car rentals, including 16 Global Fortune 500 companies in unlocking new revenue every day.

Mission

We are seeking a Senior/Staff Machine Learning Engineer with 8+ years of experience designing and deploying large-scale machine learning systems. In this role, you will be responsible for building production-ready ML solutions that drive business impact, leading the technical strategy for scalable model deployment, and mentoring engineers to adopt best practices in MLOps. You will partner closely with data scientists, product teams, and engineers to design robust, distributed ML systems that deliver measurable outcomes at scale.

Key Responsibilities

• System Architecture & Deployment – Design, build, and deploy large-scale, production-ready ML systems with a focus on reliability, scalability, and performance.
• End-to-End Ownership – Lead projects from ideation to production, including data preparation, feature engineering, training, deployment, and monitoring.
• MLOps Leadership – Define and enforce best practices for ML infrastructure, including CI/CD pipelines, model versioning, observability, and monitoring.
• Distributed ML Systems – Architect and optimize large-scale ML workflows using Spark, Kafka, and cloud-native tools.
• Model Monitoring & Optimization – Establish model monitoring frameworks, detect drift, and drive continuous improvements in accuracy, latency, and cost efficiency.
• Collaboration & Influence – Partner with product managers, engineers, and stakeholders to align ML systems with business goals.

Core Competencies

• Topgrading/Who hiring: sources and selects A-Players;
  builds diverse, high-performing teams.
• Strategic clarity: communicates a crisp plan, translates goals to weekly actions, and holds the bar.
• Ownership and bias for action: prioritizes impact, simplifies, and follows through.
• Cross-functional leadership: earns trust, influences without authority, and creates accountability.
• Analytical rigor: defines leading indicators and inspects outcomes with clear mechanisms.
• Technical depth: system design, code quality, reliability, and modern delivery practices.
• Operational excellence: incident response, observability, and continuous improvement.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related technical field.
• 8+ years of professional experience in machine learning engineering, software engineering, or applied machine learning roles.
• Expert-level proficiency in Python and strong coding skills in Java or C++.
• Proven experience with ML frameworks: TensorFlow, PyTorch, scikit-learn.
• Deep expertise in distributed systems (e.G., Spark, Kafka) and large-scale ML pipelines.
• Strong knowledge of data storage, processing, and orchestration technologies (e.G., Airflow, Databricks, Vertex AI).
• Demonstrated track record of deploying ML models in production at scale.
• Excellent problem-solving, communication, and leadership skills.
• Familiarity with Kubernetes, Docker, and cloud-native architectures.
• Hands-on experience with MLOps platforms and tooling, including:
• Vertex AI
• Databricks
• MLflow

Nice-to-Have

• Experience in Ad Tech and large-scale personalization, targeting, or recommendation systems.
• Hands-on experience with Generative AI (GenAI), RAG workflows, or AI agent systems.
• Background in real-time ML systems and streaming data applications.

We are proud to be an equal opportunity employer and are committed to providing a diverse and inclusive workplace. We welcome and encourage applications from all qualified individuals, regardless of race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.