Job Title: Generative AI Architect

Job Location: Orange, Connecticut

Job Type: Contract

Job Description

ideate/originate AI Use Cases within the organization.
Act as a strategic consultant to business leaders, facilitating AI ideation workshops to identify innovative, high-impact AI use cases.
Analyze industry trends, emerging AI technologies, and competitive landscapes to drive disruptive AI innovation within the organization.
Provide expert guidance on how AI can enhance business models, optimize operations, and create new revenue streams.
conduct AI use case feasibility assessment and develop AI solution architecture and implementation plan.
Conduct in-depth feasibility assessments of AI use cases, evaluating technical viability, data availability, and risks. Develop comprehensive technical documentation, including architecture diagrams, system specifications, and design decisions. Present AI solutions to senior stakeholders.
Collaborate with business stakeholders to translate AI opportunities into well-defined business cases with clear value propositions.
Define the technical architecture, infrastructure, and data requirements for AI initiatives. Develop high-level solution designs that outline AI models, ML pipelines, and system integrations. Provide input on technology selection, including AI frameworks, cloud platforms (Azure, AWS), and MLOps tools. Define resource and skillset requirements for AI build projects. Estimate project costs, infrastructure needs, and timelines, assisting in budget planning for AI initiatives.
lead the implementation of AI use cases, managing team of AI Engineers, Data Scientists, ML Ops engineers and others.
Ensure quality and schedule of AI use case delivery. Regularly communicate development progress with key stakeholders and receive feedback from them.
Make sure best coding practices/standards are being followed by the team, code reviews, technical troubleshooting, drive day to day development task prioritization, allocation & coordination for offshore team members via Jira based scrum process, lead technical coordination with onsite team.
Continuously optimize and tune generative models, retrieval algorithms, and pipelines to enhance performance and accuracy. Ensure AI solutions adhere to security, compliance, and privacy standards, particularly for data-sensitive applications. Drive organizational AI maturity by defining standards for model monitoring, performance evaluation, and continuous improvement.
Provide leadership in cloud architecture design, deployment, and scaling strategies, ensuring that AI systems are efficient, secure, and maintainable within the Azure/AWS ecosystem.
Governance & Risk Management – evaluate risks of AI use cases and suggest mitigations strategies.
Establish AI governance frameworks, ensuring adherence to best practices in AI ethics, model transparency, data privacy, etc.

Experience

8-10 years of experience in the ideation, design and end-to-end implementation of AI solutions.
Proven track record of leading teams of AI engineers and reporting to senior leadership.
At least 5 years of hands-on experience working with Azure AI, Azure OpenAI, Azure Cognitive Services, and Azure Machine Learning. Experience with AWS is a plus.
Proven expertise in deploying machine learning models and AI applications in a cloud environment (Azure preferred, AWS experience is a plus).

Technical Skills

Strong proficiency in Natural Language Processing (NLP) and deep learning techniques for building AI models.
Experience with large language models (LLMs) such as GPT, BERT, and OpenAI's fine-tuned models.
Knowledge of retrieval-based techniques, including semantic search, vector databases, and information retrieval systems.
Familiarity with APIs and cloud-based data storage solutions (e.g., Azure Blob Storage, Cosmos DB, etc.).
Experience with DevOps practices for machine learning, including CI/CD for AI systems, containerization (Docker), and orchestration (Kubernetes).

Additional Skills

Solid understanding of cloud architecture, scalability, and security best practices in cloud computing.
Experience in AWS cloud services is a strong advantage (e.g., AWS Lambda, S3, SageMaker).
Familiarity with data pipeline technologies and distributed data processing frameworks (e.g., Spark, Hadoop) is a plus.
Excellent problem-solving skills and the ability to work in fast-paced environments.

Soft Skills

Strong communication and collaboration skills, with the ability to explain complex AI concepts to non-technical stakeholders.
Leadership abilities to mentor teams and drive AI strategy in alignment with business goals.
Ability to work independently and in team settings, balancing multiple priorities.