Machine Learning Architect

About Quantiphi:Quantiphi is an award-winning Applied AI and Big Data software and services company, driven by a deep desire to solve transformational problems at the heart of businesses. Our signature approach combines groundbreaking machine-learning research with disciplined cloud and data-engineering practices to create breakthrough impact at unprecedented speed.Quantiphi has seen 2.5x growth YoY since its inception in 2013, we don't just innovate - we lead.Headquartered in Boston, with 4,000+ professionals across the globe. Quantiphi leverages Applied AI technologies across multiple a. Industry Verticals (Telco, BFSI, HCLS etc.) and is an established Elite/Premier Partner of NVIDIA, Google Cloud, AWS, Snowflake, and others.We've been recognized with: • 17x Google Cloud Partner of the Year awards in the last 8 years. • 3x AWS AI/ML award wins. • 3x NVIDIA Partner of the Year titles. • 2x Snowflake Partner of the Year awards. • We have also garnered top analyst recognitions from Gartner, ISG, and Everest Group. • We offer first-in-class industry solutions across Healthcare, Financial Services, Consumer Goods, Manufacturing, and more, powered by cutting-edge Generative AI and Agentic AI accelerators. • We have been certified as a Great Place to Work for the third year in a row- 2021, 2022, 2023.Be part of a trailblazing team that's shaping the future of AI, ML, and cloud innovation.Your next big opportunity starts here! For more details, visit: Website or LinkedIn Page. Role: Machine Learning ArchitectExperience Level: 10+ yrsWork Location: US (Remote) Role OverviewWe are seeking an experienced Machine Learning Architect with a strong foundation in Data Engineering to design, build, and scale enterprise AI/ML platforms and solutions. This role is responsible for defining machine learning architecture, establishing MLOps practices, and ensuring the underlying data infrastructure supports reliable, scalable, and production-ready AI systems.The ideal candidate combines expertise in machine learning, data engineering, cloud technologies, and software architecture. They will lead the design of end-to-end AI solutions, from data ingestion and feature engineering to model deployment, monitoring, and governance, while collaborating with cross-functional teams to drive AI adoption and innovation. Technical Skills • Strong expertise in Machine Learning, Deep Learning, and Statistical Modeling. • Hands-on experience with Python and ML frameworks such as PyTorch, TensorFlow, and Scikit-learn. • Experience designing and deploying scalable ML solutions on AWS, Azure, or GCP. • Strong knowledge of MLOps practices, including CI/CD, model monitoring, model versioning, and automated retraining. • Experience with containerization and orchestration technologies such as Docker and Kubernetes. • Expertise in data engineering, feature engineering, and distributed data processing frameworks. • Knowledge of Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and AI agents. • Familiarity with API design, microservices architecture, and cloud-native application development. • Understanding of AI governance, security, privacy, and Responsible AI principles. Soft Skills • Strong solution architecture and system design capabilities. • Excellent stakeholder management and communication skills. • Ability to translate business requirements into scalable AI solutions. • Leadership experience mentoring data scientists and ML engineers. • Strong analytical, problem-solving, and decision-making abilities. Key Responsibilities • Define and drive the organization's machine learning and AI architecture strategy. • Design end-to-end ML platforms, pipelines, and production-grade AI solutions. • Lead architecture decisions for predictive analytics, machine learning, and Generative AI initiatives. • Establish standards, frameworks, and best practices for ML development and deployment. • Architect scalable model serving, monitoring, and governance solutions. • Collaborate with business stakeholders to identify and prioritize AI use cases. • Guide teams through model development, deployment, and optimization activities. • Ensure AI solutions meet performance, scalability, security, and compliance requirements. • Evaluate emerging AI technologies and recommend adoption strategies. • Mentor engineering and data science teams on architecture, MLOps, and AI best practices. • Lead technical reviews, architecture governance, and solution design workshops. • Drive innovation and continuous improvement across the AI/ML ecosystem. What is in it for you: • Be part of the fastest-growing AI-first digital transformation and engineering company in the world • Be a leader of an energetic team of highly dynamic and talented individuals • Exposure to working with fortune 500 companies and innovative market disruptors • Exposure to the latest technologies related to artificial intelligence and machine learning, data and