At a Glance
- Tasks: Lead the design and deployment of cutting-edge generative AI solutions across the organization.
- Company: Join a leading firm at the forefront of AI innovation and digital transformation.
- Benefits: Enjoy flexible work options, competitive salary, and opportunities for professional growth.
- Why this job: Be part of a dynamic team driving impactful AI initiatives that shape the future of technology.
- Qualifications: Master's or Ph.D. in relevant fields with extensive experience in generative AI and machine learning.
- Other info: Collaborate with diverse teams and contribute to a culture of innovation and ethical AI practices.
The predicted salary is between 72000 - 108000 £ per year.
WHAT YOU’LL DO
he Generative AI Architect – Senior Manager plays a crucial role in our AI Center of Excellence (CoE), spearheading the design, deployment, and governance of generative AI solutions across the organization. This senior role demands a unique blend of technical expertise in AI, strategic vision, and hands-on experience with generative models, ensuring that AI initiatives drive business impact and align with organizational goals. The Generative AI Architect will work closely with diverse stakeholders-digital teams, enterprise architects, and governance bodies-to deliver scalable, ethical, and high-performance AI solutions that meet the highest standards across projects.
Key Responsibilities:
- End-to-End Generative AI Architecture: Collaborate with digital teams, architects, and stakeholders to architect comprehensive AI solutions within the enterprise. Partner closely with teams, including Team Builder, Career Development (CD) Platform, and RevOps, to ensure seamless integration of AI solutions, maximizing their effectiveness across various business functions.
- Solution Architecture for Early-Stage Initiatives: Lead the architectural design for early-stage generative AI projects, guiding proof-of-concept (PoC) and prototype development with the AI CoE. Prepare these projects for review by the Investment Committee (IC) to secure funding.
- Cloud Engineering: Implement deployment strategies such as containerization, DevSecOps, CI/CD, performance monitoring, and logging using tools like Docker, Docker Compose, Kubernetes, and GitHub Actions.
- API Management and Deployment: Oversee REST and GraphQL API design, model serving, and endpoint creation with tools like Flask and FastAPI.
- Cloud-Based Model Deployment: Leverage cloud platforms like AWS (Lambda, S3, EC2, SageMaker) and Azure ML to facilitate scalable model deployment.
- AI/ML Lifecycle Management: Manage model development, experiment tracking, version control, and deployment using tools such as MLflow, DVC, and Weights & Biases.
- AI Governance and Risk Assessment: Actively engage with AI Governance frameworks and the Enterprise Architecture CoE to:
- Assess generative AI solutions across digital teams, identifying potential risks such as technical debt or overlapping initiatives.
- Evaluate generative AI proposals for IC review, ensuring alignment with organizational standards and assessing feasibility, stack compatibility, and build-vs-buy decisions.
- Responsible AI Implementation: Partner with the Generative AI Platforms team to ensure Responsible AI principles and standards are woven into LLM evaluations and applications, promoting ethical and secure AI deployment.
- Cross-Functional Collaboration: Serve as a subject matter expert on generative AI, guiding cross-functional teams, including digital, platform, and governance stakeholders. Promote knowledge-sharing, establish best practices, and contribute to a collaborative, AI-centric culture.
- Strategic Planning & Innovation: Develop and execute architectural strategies and roadmaps in alignment with the company’s vision. Stay updated on industry advancements to drive AI innovation and integrate cutting-edge practices.
- Security and Compliance: Ensure that AI solutions comply with security protocols and regulatory standards, implementing necessary safeguards to mitigate AI-related risks.
- Go-To-Market Strategy (GTM): Collaborate with onsite teams and senior architects to define go-to-market strategies for AI solutions, aligning development with client needs for impactful, effective implementations.
YOU BRING (EXPERIENCE & QUALIFICATIONS)
Required Skills & Qualifications:
- Educational Background: Master’s or Ph.D. in Computer Science, Machine Learning, AI, Data Science, or a related discipline.
- Technical Expertise: Extensive experience in generative AI and machine learning frameworks, including models like GANs, VAEs, transformer-based architectures, and LLMs.
- Solution and Enterprise Architecture Experience: Proven track record in designing and deploying AI architectures at scale, with strong capabilities in risk assessment and alignment with enterprise standards.
- Governance and Standards Alignment: Experience in establishing or working within AI governance frameworks, focusing on risk mitigation, Responsible AI compliance, and adherence to organizational standards.
- Leadership and Communication Skills: Exceptional interpersonal, leadership, and communication abilities, with a demonstrated capacity to work across teams, lead cross-functional projects, and communicate complex technical concepts to non-technical stakeholders.
Preferred Qualifications:
- Cloud & MLOps Expertise: Proficiency in MLOps and AI lifecycle management, particularly for scalable deployments on platforms such as AWS, GCP, and Azure.
- Responsible AI Knowledge: In-depth understanding of Responsible AI practices, particularly in generative AI, and experience in designing and implementing these within enterprise frameworks.
- Thought Leadership: Contributions to AI and generative technologies through publications, presentations, or industry engagement are a strong advantage.
- Technical Proficiency: Hands-on experience with cloud-based AI/ML solutions and programming languages (e.g., Python, PySpark), data modeling, and microservices. Experience with LLM orchestration on platforms like OpenAI on Azure, AWS Bedrock, GCP Vertex AI, or Gemini AI, alongside familiarity with MLOps tools (MLFlow, batch prediction, real-time endpoints) and DevOps tools like Azure DevOps, GitHub Actions, Jenkins, Terraform, and AWS CloudFormation.
YOU’LL WORK WITH
BCG’s information technology group collaboratively delivers the latest digital technologies that enable our consultants to lead and our business to grow. For our IT jobs, we seek individuals with expertise in the areas of IT infrastructure, application development, business systems, collaborative and social technologies, information security, and project leadership.
Generative AI Architect - Senior Manager employer: Boston Consulting Group
Contact Detail:
Boston Consulting Group Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Generative AI Architect - Senior Manager
✨Tip Number 1
Familiarize yourself with the latest trends and advancements in generative AI. This will not only help you understand the current landscape but also allow you to speak confidently about how your skills align with our needs during discussions.
✨Tip Number 2
Network with professionals in the AI field, especially those who have experience in governance and compliance. Engaging with industry experts can provide insights into best practices and may even lead to referrals.
✨Tip Number 3
Prepare to discuss specific projects where you've implemented generative AI solutions. Be ready to explain your role, the challenges faced, and how you ensured alignment with organizational goals and standards.
✨Tip Number 4
Showcase your leadership and communication skills by participating in relevant forums or webinars. This demonstrates your commitment to the field and helps build your reputation as a thought leader in generative AI.
We think you need these skills to ace Generative AI Architect - Senior Manager
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in generative AI and machine learning frameworks. Focus on specific projects where you've designed and deployed AI architectures, emphasizing your leadership and communication skills.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and how your background aligns with the responsibilities of the Generative AI Architect role. Mention your experience with cloud platforms and governance frameworks, and how you can contribute to the company's vision.
Showcase Relevant Projects: Include examples of relevant projects in your application that demonstrate your technical expertise and strategic vision in generative AI. Highlight any contributions to AI governance or responsible AI practices.
Highlight Collaboration Skills: Since this role involves cross-functional collaboration, emphasize your ability to work with diverse teams. Provide examples of how you've successfully led projects involving multiple stakeholders and communicated complex concepts effectively.
How to prepare for a job interview at Boston Consulting Group
✨Showcase Your Technical Expertise
Be prepared to discuss your hands-on experience with generative AI models and frameworks. Highlight specific projects where you've implemented solutions using GANs, VAEs, or transformer-based architectures, and be ready to explain the impact of these projects on business outcomes.
✨Demonstrate Strategic Vision
Articulate your understanding of how generative AI can drive business impact. Discuss your approach to aligning AI initiatives with organizational goals and how you would develop architectural strategies that support innovation while ensuring compliance with governance standards.
✨Prepare for Cross-Functional Collaboration
Since this role involves working closely with diverse stakeholders, prepare examples of how you've successfully collaborated across teams. Emphasize your leadership and communication skills, particularly in translating complex technical concepts for non-technical audiences.
✨Discuss Responsible AI Practices
Be ready to talk about your knowledge of Responsible AI principles and how you've integrated them into your previous work. Share insights on risk assessment and governance frameworks you've worked with, and how you ensure ethical AI deployment in your projects.