# Yoh - Sr. AI Architect

JD_URL: Pasted JD text from Keith in Slack on 2026-07-30 16:56 UTC

JD_URL handed to jd-resume-fit-optimizer: Pasted JD text from Keith in Slack on 2026-07-30 16:56 UTC

Location/work model: Boston, MA - 2 days onsite

Posted/source timing: Pasted by Keith on 2026-07-30

Caveat: Hybrid Boston role requiring 2 days onsite; salary is not disclosed. Strong enterprise AI architecture, MLOps, RAG/LLM orchestration, agent workflow, time-series forecasting/optimization, cloud/data platform, governance, and executive-stakeholder fit; direct energy trading/domain experience is the main caveat.

## Captured JD

Sr. AI Architect
Location: Boston, MA - 2 days onsite
Direct Hire/Full Time

Position Overview
We are seeking a highly skilled AI Architect to lead the design, development, and implementation of production enterprise grade artificial intelligence solutions that support power trading, asset optimization, risk analysis, and decision support. In this role, you will define the architectural vision for AI and machine learning platforms, guide cross-functional engineering & analytics teams, and ensure solutions are scalable, secure, and aligned with business objectives. The ideal candidate combines deep technical mastery with strategic thinking and strong leadership capabilities.

Key Responsibilities
AI Strategy & Architecture
- Define and maintain the enterprise AI architecture, ensuring alignment with organizational goals and technology roadmaps.
- Lead the design of scalable, robust, and secure AI/ML systems, platforms, and data pipelines.
- Evaluate and select AI technologies, frameworks, cloud services, and tools to support solution development.
- Develop reference architectures, best practices, and governance models for AI and ML solutions.

Solution Design & Delivery
- Architect end-to-end AI/ML solutions, from data ingestion and feature engineering to model deployment and monitoring.
- Partner with product, data, and engineering teams to translate business problems into AI-based solutions.
- Oversee MLOps implementation, ensuring continuous integration/continuous delivery pipelines for machine learning models.
- Establish and enforce standards for model lifecycle management, versioning, observability, and performance tuning.

Data & Cloud Engineering Integration
- Collaborate with data engineers to design data models, ETL/ELT pipelines, and real-time streaming architectures.
- Architect AI platforms using cloud technologies, with Azure preferred and AWS or GCP experience a plus.
- Ensure AI systems meet enterprise standards for security, privacy, compliance, and reliability.

Leadership & Collaboration
- Provide technical leadership, mentorship, and guidance to AI engineers, data scientists, and cross-functional teams.
- Champion AI innovation by staying current with emerging technologies, research advancements, and industry practices.
- Communicate technical concepts to executives, business stakeholders, and non-technical audiences.

Required Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related field.
- 8+ years of experience in Software Engineering, Data Engineering, or Machine Learning, including 3+ years in an AI architecture leadership role.
- Hands-on expertise with TensorFlow, PyTorch, Scikit learn, LangChain, time-series modeling and forecasting, RAG systems, LLM orchestration pipelines, agent-based AI workflows, AI/ML cloud services including Azure Machine Learning, AWS Sagemaker, or GCP Vertex AI, modern data platforms including Databricks, Snowflake, Synapse, BigQuery, distributed systems and container orchestration including Docker and Kubernetes.
- Strong knowledge of MLOps, CI/CD, and model governance frameworks.
- Proficiency with Python, SQL, and at least one other programming language.
- Understanding of security, compliance, data governance, and responsible AI practices.

Preferred Qualifications
- Experience with large language models, vector databases, and generative AI.
- Prior experience in energy trading firms, utilities, IPPs, or commodities trading.
- Optimization models or market simulation frameworks.
- Experience integrating AI into trader tools, risk and analytics platforms, and decision-support workflows.
- Experience with vector databases and semantic search, feature stores and model registries, LLM evaluation, guardrails, and observability, technical leadership or architecture ownership across teams.
- Certifications in cloud architecture.
- Experience implementing real-time AI and high-scale distributed systems.
- Familiarity with enterprise architecture frameworks.

Key Competencies
- Strategic thinking with an innovative mindset.
- Strong problem-solving and analytical skills.
- Excellent communication and stakeholder management.
- Ability to lead and influence without authority.
- Passion for emerging AI technologies and continuous learning.