Lead Architect - Generative AI

Lead Architect - Generative AI page is loaded## Lead Architect - Generative AI locations:
Estados Unidos, Massachusetts, Boston:
CT - Orange - 180 Marsh Hill Rdposted on:
Publicado hoytime left to apply:
Fecha final: 31 de diciembre de 2025 (Quedan más de 30 días para realizar la solicitud) job requisition :
R-24306
** The base salary for this position is dependent upon experience and location.
**** Locations:
Orange, CT or Boston, MA - (In office 5 days per week)
**** Salary range: $ to $
** 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. The ideal candidate brings deep expertise in cloud-native AI services, software architecture, and machine learning frameworks, along with strong communication and mentoring skills.

Reporting to the Senior Manager – IT Infrastructure, this role drives ethical AI adoption and delivers transformative solutions that enhance efficiency, customer experience, and competitive edge.
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* Key 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.
* Proven ability to manage cross-functional teams, including data scientists, software engineers, and Dev Ops professionals.
* Provides agile methodologies (Scrum, Kanban) and tools like Jira or Trello for project tracking.
* Aligns AI initiatives with business goals and drive innovation through generative AI technologies.
* Problem-solves to address technical challenges and ensure timely delivery of projects.
* Effectively communicates to bridge technical and non-technical stakeholders, including executives, product managers, and clients.
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* Required Qualifications:

*** 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.
* Familiarity with fine-tuning, prompt engineering, and managing model outputs for scalability and accuracy.
* Proficiency in Python for AI/ML workflows, including libraries like Tensor Flow, PyTorch, Hugging Face, and Lang Chain.
* Strong command of Java for backend development, microservices, or integrating AI models into enterprise systems.
* Experience with AWS (especially Bedrock, Sage Maker, 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, Tensor Flow, PyTorch) and tools for model deployment (e.g., Docker,…