Architect ML Engineer

Quantiphi • Boston, MA

4d

About The Position

Quantiphi is hiring an Architect ML Engineer to provide senior technical leadership for critical AI initiatives. The role governs architectural quality and strategic direction, designing enterprise-grade, scalable, and secure AI/ML platforms across Generative AI, agentic systems, traditional machine learning, and computer vision. It is a highly visible, client-facing role involving collaboration with senior client leaders and travel to client sites, Cambridge, and Pittsburgh. The preferred location is Cambridge, Massachusetts.

The architect remains hands-on for 25% to 50% of the time, prototyping, writing code for complex scenarios, and helping engineering teams resolve technical blockers. The role also leads solution design and pre-sales proposals, establishes practice-wide architecture and MLOps/LLMOps standards, and mentors architects and senior engineers. The position focuses on enterprise AI in healthcare and life sciences, including high availability, security, scalability, and regulatory compliance.

Requirements

• At least 8 years of progressive experience in AI/ML engineering, including at least 3 years in a technical leadership or architectural role.
• Deep expertise in enterprise architecture and system design, including system integration, legacy modernization, cloud-native design, and API gateway architectures.
• Ability to visually represent complex, multi-system architectures and explain technical trade-offs with structured reasoning.
• Mastery of statistical machine learning, deep learning architectures such as Transformers, CNNs, and RNNs, and computer vision platforms.
• Proficiency in Python and machine learning frameworks such as PyTorch or TensorFlow.
• Deep understanding of agent development lifecycles, multi-agent system design, and A2A communication protocols.
• Hands-on experience with agentic AI frameworks such as LangGraph, CrewAI, AutoGen, LlamaIndex, or LangChain.
• Experience with Model Context Protocol, prompt engineering strategies such as Chain of Thought and Tree of Thoughts, and LLM optimization methods including PEFT, SFT, QLoRA, and DPO.
• Practical experience implementing vector databases such as Pinecone, Milvus, Qdrant, or OpenSearch.
• Experience designing end-to-end ML lifecycles, including data ingestion, feature stores, registries, deployment, monitoring, and drift detection.
• Strong SQL and data transformation experience using BigQuery, Snowflake, Databricks, or Spark.
• Proven experience designing secure, compliant architectures aligned with enterprise regulatory frameworks such as SOC 2, GDPR, or ISO 27001.
• Exceptional technical reasoning and probing skills, including the ability to justify architectural choices and evaluate alternative tooling stacks.
• Exceptional presentation and communication skills, with the ability to pitch proposals, influence decision-makers, and build stakeholder relationships.
• A hands-on leadership approach, including willingness to write code, build prototypes, and guide engineers through technical uncertainty.
• Passion for coaching architects and senior engineers, establishing standards, and fostering technical excellence.
• Willingness to visit client sites and travel to Cambridge and Pittsburgh.

Nice To Haves

• Professional Cloud Architect and Machine Learning Engineer certifications on AWS or GCP.
• Publications, technical blogs, or conference presentations in agentic or generative AI or enterprise architecture.

Responsibilities

• Design and govern large-scale, mission-critical AI platforms that meet availability, security, scalability, and compliance requirements in healthcare and life sciences.
• Serve as the primary technical contact for strategic client accounts and collaborate with senior client stakeholders.
• Lead pre-sales solutioning, design solutions, prepare technical proposals, and present them to stakeholders.
• Remain hands-on for 25% to 50% of the time by building prototypes, designing core frameworks, writing production code, and debugging complex system issues.
• Architect enterprise-grade agentic systems, multi-agent platforms, autonomous workflow orchestration, and memory/context management using Model Context Protocol components.
• Establish LLMOps and MLOps standards, including prompt versioning, automated evaluation, CI/CD, drift detection, and production monitoring.
• Define reference architectures and reusable frameworks to accelerate project delivery.
• Design AI security architectures for regulated industries and ensure alignment with applicable compliance and security requirements.
• Mentor Associate Technical Architects, senior engineers, and delivery teams; conduct architectural reviews and manage technical debt.

Benefits

• Opportunity to work at an AI-first digital transformation and engineering company.
• Leadership role on a team of dynamic and talented individuals.
• Exposure to Fortune 500 companies and innovative market disruptors.
• Exposure to current technologies in artificial intelligence, machine learning, data, and cloud.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed