Senior Machine Learning Engineer

We are looking for a Lead Machine Learning Engineer to drive the design, development, deployment, and scalability of Maya’s machine learning platforms and solutions. This role will lead the engineering efforts that enable Data Scientists to efficiently build, deploy, and operate production-grade AI and machine learning solutions.

As a technical leader, you will oversee the architecture and implementation of ML platforms, data pipelines, and model-serving infrastructure while ensuring engineering excellence, operational reliability, and alignment with business objectives. You will also play a critical role in project delivery, stakeholder management, and the growth and mentorship of Machine Learning Engineers across the team.

NATURE OF WORK& KEY RESPONSIBILITIES:

Technical Leadership & Solution Delivery

• Lead the end-to-end delivery of machine learning initiatives, from requirements definition and architecture design to production deployment and ongoing support.
• Collaborate closely with Data Scientists, Product Owners, Engineering teams, and business stakeholders to translate business objectives into scalable ML-powered solutions and product features.
• Architect, design, and oversee the development of platforms, services, and infrastructure that support machine learning development, deployment, and monitoring.
• Design and implement robust model-serving architectures, balancing model performance, scalability, latency, and cost considerations.
• Drive the development of reusable frameworks, tools, and software libraries that improve Data Scientists’ productivity and accelerate model development cycles.
• Lead the design and implementation of scalable data pipelines and ML workflows supporting production environments.

Platform & Infrastructure Engineering

• Oversee the development and optimization of CI/CD pipelines for machine learning services, data pipelines, and platform infrastructure.
• Design reliable, scalable, secure, and highly observable infrastructure for data and machine learning platforms.
• Establish engineering standards, architectural best practices, coding guidelines, and operational processes across the ML engineering function.
• Ensure ML systems meet performance, reliability, security, and maintainability requirements.

Team Leadership & Stakeholder Management

• Conduct architecture reviews, code reviews, and technical design discussions to maintain high engineering standards.
• Mentor and develop Machine Learning Engineers through coaching, technical guidance, and career development support.
• Lead project planning, execution, prioritization, and risk management activities.
• Coordinate cross-functional initiatives and proactively manage stakeholder expectations to ensure successful project delivery.
• Identify project dependencies, technical risks, and operational bottlenecks and drive mitigation strategies.

REQUIRED QUALIFICATIONS

• Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or another quantitative discipline.
• At least 6+ years of experiencebuilding, deploying, and operating data pipelines and machine learning systems in production environments.
• At least 3+ years of experienceleading technical initiatives, projects, or engineering teams.
• At least 3+ years of experiencedesigning and implementing CI/CD pipelines and MLOps practices.
• At least 3+ years of experiencedeveloping and deploying microservices and distributed systems.
• At least 3+ years of experienceworking with cloud platforms such as AWS, GCP, or Azure.
• Experience with Databricks, Apache Spark, or large-scale data processing frameworks.
• Demonstrated success in managing stakeholders, driving cross-functional initiatives, and delivering business impact through technology.
• Proven experience mentoring engineers and influencing technical direction across teams.

Preferred Qualifications

• Experience supporting AI/ML use cases in fintech, digital banking, e-commerce, or high-growth technology organizations.
• Experience building internal ML platforms, feature stores, model serving platforms, or MLOps infrastructure.
• Experience with GenAI, LLM deployment, vector databases, and AI platform engineering is a plus.