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Generative Ai Project Manager Jobs in Connecticut

Staffingine LLC

Generative AI Architect

Orange, CT · On-site

Estimate project costs, infrastructure needs, and timelines, assisting in budget planning for AI ... Governance & Risk Management - evaluate risks of AI use cases and suggest mitigations strategies.

Deloitte

Oracle Generative AI Manager

Stamford, CT

Deloitte Oracle Generative AI Architect Managers help clients delineate strategy and vision, design and implement process and systems which align with business objectives and have a measurable impact ...

Deloitte

Oracle Generative AI Manager

Hartford, CT

Deloitte Oracle Generative AI Architect Managers help clients delineate strategy and vision, design and implement process and systems which align with business objectives and have a measurable impact ...

Deloitte

Oracle Generative AI Manager

Stamford, CT

Deloitte Oracle Generative AI Architect Managers help clients delineate strategy and vision, design and implement process and systems which align with business objectives and have a measurable impact ...

The Hartford

Lead Data Engineer - Generative AI

Hartford, CT · Hybrid

$115.50K - $138.70K/yr

... management. Implement best practices in reliability engineering, including redundancy, fault ... Contributions to open-source AI projects or research publications in the field of Generative AI.

Deloitte

FinanceAI Senior Consultant

Hartford, CT

$117.50K/yr

Experience defining and measuring KPIs and metrics for generative and predictive AI projects to ... Additionally, we manage risks and opportunities associated with diverse and complex grant programs ...

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Generative Ai Project Manager information

What are the key skills and qualifications needed to thrive as a Generative AI Project Manager, and why are they important?

To thrive as a Generative AI Project Manager, you need a solid understanding of AI/machine learning concepts, project management methodologies, and a relevant degree (such as computer science or engineering). Familiarity with tools like Jira, Agile frameworks, and AI platforms (e.g., TensorFlow, PyTorch) as well as certifications like PMP or Agile Scrum Master are highly beneficial. Strong leadership, communication, and problem-solving skills set outstanding candidates apart by enabling them to bridge technical and non-technical teams. These abilities are crucial for delivering AI projects on time, ensuring alignment with business goals, and adapting to rapidly evolving technology landscapes.

What are some unique challenges faced by Generative AI Project Managers when overseeing cross-functional teams?

Generative AI Project Managers often encounter the challenge of bridging knowledge gaps between technical AI specialists, such as data scientists and engineers, and non-technical stakeholders, like product managers or business leaders. Coordinating clear communication and aligning project goals requires balancing rapid technological changes with business requirements, all while ensuring ethical and responsible AI development. Additionally, managing timelines can be complex due to the experimental nature of generative AI projects, which may involve iterative prototyping and unexpected roadblocks. Building trust and facilitating collaboration across diverse teams is key to project success.

What does a Generative AI Project Manager do?

A Generative AI Project Manager oversees projects that involve the development and implementation of generative artificial intelligence solutions. Their responsibilities include coordinating teams of data scientists, engineers, and designers, managing project timelines and budgets, and ensuring that deliverables meet business objectives. They also facilitate communication between technical and non-technical stakeholders to ensure alignment and project success. Additionally, they stay updated on advances in AI technology to guide project direction and innovation.

What is the difference between Generative Ai Project Manager vs Data Scientist?

AspectGenerative Ai Project ManagerData Scientist
Required CredentialsProject management certifications, AI knowledgeDegree in Data Science, Computer Science, or related fields
Work EnvironmentCross-functional teams, project planningData analysis, model development
Employer & Industry UsageTech companies, AI startups, R&D departmentsTech firms, research institutions, analytics companies

While both roles involve AI, the Generative Ai Project Manager oversees AI projects, coordinating teams and timelines, whereas the Data Scientist focuses on analyzing data and building models. The project manager ensures project delivery, while the data scientist develops the AI models used within projects.

What are popular job titles related to Generative Ai Project Manager jobs in Connecticut? For Generative Ai Project Manager jobs in Connecticut, the most frequently searched job titles are:
What job categories do people searching Generative Ai Project Manager jobs in Connecticut look for? The top searched job categories for Generative Ai Project Manager jobs in Connecticut are:
Generative Ai Project Manager jobs near you
Generative AI Architect

Generative AI Architect

Staffingine LLC

Orange, CT • On-site

Contractor

Posted 13 days ago

Job description

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.