Job Description

Appealing Points:

• Work with cutting-edge MLOps and Generative AI technologies : Contribute to building scalable infrastructure on Azure and develop enterprise-grade GenAI applications using LLMs, RAG, and orchestration tools like LangChain.
• Blend leadership with hands-on technical impact : Lead and mentor a team while taking ownership of production ML systems, including CI/CD/CT pipelines, monitoring, and reliability engineering.
• Make a real-world impact in financial and insurance domains : Play a key role in delivering responsible, scalable AI systems in industries where accuracy, compliance, and trust are critical.

Annual salary: 8 Million and above

Job Responsibilities:

• Lead, mentor, and grow a team of junior MLOps engineers, fostering a culture of engineering excellence and collaboration.
• Design, build, and maintain a scalable MLOps infrastructure on Azure, enabling the entire lifecycle of machine learning models—from data preparation and training to deployment and monitoring.
• Take full ownership of the operational stability and reliability of production ML systems. This includes leading incident response for pipeline failures, troubleshooting issues immediately, and implementing robust, recoverable, and self-healing solutions to ensure seamless integration with core business operations.
• Architect and implement CI/CD/CT (Continuous Integration/Continuous Delivery/Continuous Training) pipelines for real-time, near-real-time, and batch ML systems using services like Azure Kubernetes Service (AKS) and serverless compute.
• Establish and own the model monitoring strategy, implementing robust systems to track model performance, detect data drift, and trigger retraining pipelines to maintain accuracy and reliability.
• Partner with data scientists to migrate ML models from development environments to production-grade code, focusing on performance, scalability, and maintainability.
• Develop and manage enterprise-grade Generative AI applications using Large Language Models (LLMs), Retrieval Augmented Generation (RAG), and multi-agent frameworks.
• Collaborate with the data platform and IT teams to resolve dependencies and ensure a seamless integration of ML systems with the broader tech stack, including platforms like Azure Synapse Analytics.
• Ensure the responsible use of AI practices, adhering to ethical guidelines, fairness, and compliance standards in all deployed models.
• Gain hands-on experience with the Domino MLOps platform, contributing to its integration and utilization.
• Engage in data science development tasks, leveraging your expertise to contribute to the full project lifecycle.
• Communicate technical strategy, project status, and outcomes effectively to both technical teams and business stakeholders.

Job Qualification:

• Bachelor's degree in Computer Science, Engineering, Statistics, or a related quantitative field. An advanced degree is a plus.
• Azure certifications are highly valued, particularly Azure Data Scientist Associate, Azure DevOps Engineer Expert, Azure Data Engineer, or Azure AI Engineer Associate.
• Certifications or specialized training in MLOps, GenAI, or LLMs are desirable.
• 5+ years of experience in software engineering, DevOps, or data science, with at least 3 years focused on MLOps and building production ML systems.
• Proven experience leading technical teams and mentoring junior engineers.
• Hands-on experience building and managing ML infrastructure and CI/CD pipelines on cloud platforms, especially Azure.
• Demonstrated experience deploying and managing a variety of ML models (e.g., classification, regression, LLMs) in a production environment.
• Experience with the Domino MLOps platform is a significant plus.
• Background in financial or insurance domains is highly desirable.

Skills -

• MLOps & DevOps: Expertise in MLOps principles and CI/CD practices. Hands-on experience with tools like Azure DevOps, GitHub Actions, Docker, and container orchestration with  Azure Kubernetes Service (AKS) .
• Cloud Platform: Deep expertise in  Azure Machine Learning (AzureML)and its ecosystem, including  Azure Synapse Analytics , Azure Data Factory, and  serverless computing (e.g., Azure Functions) .
• Programming: Advanced proficiency in Python and SQL. Experience with shell scripting.
• ML & Data Science: Strong understanding of machine learning concepts and libraries (e.g., Scikit-learn, TensorFlow/PyTorch). Hands-on experience with large-scale data processing tools like  PySpark .
• Generative AI: Practical experience implementing and optimizing applications using LLMs, RAG architectures, vector databases, and orchestration frameworks (e.g., LangChain).
• Monitoring: Experience with monitoring and observability tools for ML models 
• AI-Assisted Development: Proficiency with tools like GitHub Copilot to boost productivity

Language Skills: Business level Japanese (JLPT N2) and Business level English

Company Description 

Our Client has operations in more than 40 countries and holds leading market positions in the United States, Japan, Latin America, Asia, Europe, and the Middle East. We are ranked #43 on the Fortune 500 list for 2018.With over 150 years of experience, the companies offer life, accident and health insurance, retirement, andsavings products through agents, third-party distributors such as banks and brokers, and direct marketing channels. Our name is recognized and trusted by approximately 100 million customers worldwide and we serve more than 90 of the top 100 FORTUNE 500 companies in the United States.

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