Associate Architect - MLE

About the role

Quantiphi is seeking an experienced Associate Architect – Machine Learning Engineering (MLE) to design and deliver scalable AI/ML platforms, developer frameworks, and Agentic AI solutions for enterprise applications. In this role, you will provide technical leadership in building production-grade machine learning systems, evolving Python SDK frameworks, and enabling intelligent autonomous workflows. You will work closely with product, platform, and engineering teams to define architecture, establish best practices, and build highly scalable AI solutions leveraging modern agentic frameworks.

Responsibilities

• Architect and evolve enterprise-grade Python SDK frameworks that improve developer productivity, extensibility, and maintainability across AI platforms.
• Design scalable Agentic AI architectures and multi-agent systems capable of orchestrating complex business workflows with minimal human intervention.
• Lead the design and implementation of reusable AI platform components, ensuring high performance, reliability, and security.
• Build and optimize high-performance RESTful APIs using FastAPI to support AI services, inference pipelines, and autonomous agents.
• Define architectural standards, coding guidelines, and engineering best practices for ML platforms and SDK development.
• Partner with Data Science, Product, Platform Engineering, and Cloud teams to translate business requirements into scalable technical solutions.
• Establish observability, monitoring, and evaluation strategies using Galileo to improve model quality, agent performance, and production reliability.
• Drive architectural decisions around scalability, resiliency, performance optimization, and software lifecycle management.
• Mentor Machine Learning Engineers through technical guidance, design reviews, and best practices.
• Evaluate emerging AI technologies and recommend architectural improvements to enhance enterprise AI capabilities.
• Support production environments by troubleshooting complex distributed systems and ensuring high platform availability.

Requirements

• Expert-level proficiency in Python with extensive experience building enterprise-grade machine learning applications.
• Strong experience designing scalable APIs using FastAPI.
• Deep understanding of software engineering principles, object-oriented programming, and API design.
• Hands-on experience designing and implementing Agentic AI workflows and multi-agent systems.
• Experience building production-ready AI applications using modern Agentic Frameworks.
• Strong understanding of LLM orchestration, autonomous agents, and AI workflow automation.
• Extensive experience developing and maintaining Python SDKs or internal developer platforms.
• Experience creating reusable frameworks, libraries, and tooling used across engineering organizations.
• Strong experience implementing automated CI/CD pipelines using Jenkins and GitLab Runners.
• Experience with release automation, artifact management, and deployment strategies.
• Experience using Galileo for AI evaluation, observability, and production monitoring.
• Knowledge of monitoring AI systems, debugging model behavior, and improving production performance.
• Proven experience designing scalable, cloud-native AI platforms and distributed machine learning systems.
• Strong understanding of microservices architecture, system scalability, security, and performance optimization.
• Strong architectural thinking with excellent problem-solving abilities.
• Able to lead technical discussions and influence architectural decisions across teams.
• Excellent communication and stakeholder management skills.
• Proven ability to mentor engineers and foster technical excellence.
• Able to independently drive large-scale engineering initiatives from design through production.

Qualifications

• Experience with LLM-based applications and Agentic AI platforms.
• Experience with Docker and Kubernetes.
• Knowledge of cloud platforms such as AWS, Google Cloud Platform (GCP), or Azure.
• Experience with MLOps tools and production ML deployment.
• Experience with distributed systems and event-driven architectures.
• Contributions to open-source AI, Python SDK, or Agentic AI projects.
• Experience with infrastructure-as-code (Terraform or similar).