Lead Architect - Generative AI

About the role

The Lead Architect – Generative AI (Artificial Intelligence) is a senior technical leader responsible for shaping the organization's AI strategy and delivering innovative solutions using generative AI technologies. This role focuses on architecting scalable, secure, and high-performance AI systems across AWS and Azure, with strong emphasis on infrastructure automation via Terraform. Collaborating with data scientists, engineers, and business stakeholders, the architect integrates generative AI—such as LLMs, NLP, image synthesis, and code generation—into enterprise platforms.

Responsibilities

• Designs and develops AI architectures and solutions using AWS and Azure.
• Collaborates with cross-functional teams to integrate AI capabilities into existing systems.
• Optimizes AI models for performance and scalability.
• Implements infrastructure as code using Terraform.
• Remains up to date with the latest advancements in AI and cloud technologies.
• Provides technical leadership and mentorship to junior team members.
• Promotes agile methodologies (Scrum, Kanban) and tools like Jira or Trello for project tracking.
• Aligns AI initiatives with business goals and drives innovation through generative AI technologies.
• Solves technical challenges and ensures timely delivery of projects.
• Effectively communicates to bridge technical and non-technical stakeholders, including executives, product managers, and clients.

Requirements

• Bachelor’s degree in Engineering, Computer Science, Information Technology, or a related field and a minimum of 10 years of relevant experience.
• An equivalent combination of education and experience may be considered.
• Relevant experience with Application Development, Architecture or IT.
• Experience with Terraform for infrastructure automation.
• Proficiency in programming languages such as Python, Java, C++, and Net.
• Familiarity with use cases such as natural language processing (NLP), content generation, chatbots, code generation, and image synthesis.
• Knowledge of ethical considerations in AI, including bias mitigation, fairness, and compliance with regulations like GDPR or CCPA.
• Awareness of the latest advancements in generative AI, LLMs (Large Language Models), and multi-modal models.
• Excellent problem-solving skills and attention to detail.
• Strong communication and teamwork abilities.
• Extensive experience with OpenAI APIs (e.g., GPT models, DALL·E, Whisper) and Amazon Bedrock for building and deploying generative AI solutions.
• Proficiency in Python for AI/ML workflows, including libraries like TensorFlow, PyTorch, Hugging Face, and LangChain.
• Strong command of Java for backend development, microservices, or integrating AI models into enterprise systems.
• Experience with AWS (especially Bedrock, SageMaker, Lambda, and S3) and familiarity with other cloud providers like Azure or Google Cloud if needed.
• Hands-on experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) and tools for model deployment (e.g., Docker, Kubernetes).
• Expertise in designing and integrating RESTful APIs and GraphQL for AI model consumption.

Qualifications

• Master's degree in a related field.
• Experience with other cloud platforms and AI tools.
• Knowledge of machine learning frameworks and libraries.

Skills

• Deep expertise in cloud-native AI services, software architecture, and machine learning frameworks.
• Strong communication and mentoring skills.
• Proven ability to manage cross-functional teams, including data scientists, software engineers, and DevOps professionals.
• Problem-solving skills and attention to detail.
• Excellent communication and teamwork abilities.
• Extensive experience with OpenAI APIs (e.g., GPT models, DALL·E, Whisper) and Amazon Bedrock for building and deploying generative AI solutions.
• Proficiency in Python for AI/ML workflows, including libraries like TensorFlow, PyTorch, Hugging Face, and LangChain.
• Strong command of Java for backend development, microservices, or integrating AI models into enterprise systems.
• Experience with AWS (especially Bedrock, SageMaker, Lambda, and S3) and familiarity with other cloud providers like Azure or Google Cloud if needed.
• Hands-on experience with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch) and tools for model deployment (e.g., Docker, Kubernetes).
• Expertise in designing and integrating RESTful APIs and GraphQL for AI model consumption.

Benefits

Comprehensive benefits package including health insurance, retirement plans, paid time off, and more.

Pay

The base salary range for this position is dependent upon experience and location, ranging from $125,766 - $164,354.

Schedule

This role requires an in-office presence in Boston, MA, and Orange, CT.