AIML Architect

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Proposed designation:
Associate Director - AI Architect

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Reporting to:
Director, Chief Architect

Primary Skills:

• Machine Learning (ML) / Deep Learning (DL)
• Natural Language Processing (NLP) / Computer Vision / Reinforcement Learning
• MLOps / ML Pipelines
• Data Engineering / Feature Engineering
• Model Optimization / Quantization / Pruning
• Cloud/ Azure / AWS / GCP
• TensorFlow / PyTorch / Hugging Face
• LangChain / LamaIndex / CrewAI
• OpenAI / LLama / Mistral / Claude
• Kubeflow / MLflow / ONNX
• Docker / Kubernetes
• Gen AI/ Agents / RAG

This role is crucial for
designing, architecting, and implementing advanced AI solutions t
hat align with strategic objectives. The AI Technology Architect will be responsible for the technical design and solution architecture of AI projects, working closely with development teams and being hands-on with the latest AI technologies.

Key responsibilities include:

1. Solution Architecture and Technical Design:

• Design and architect end-to-end AI solutions, including data pipelines, model development, deployment strategies, and integration with existing systems.

• Define the technical components, services, and libraries required for AI projects, ensuring scalability, security, and performance.

• Lead the selection of appropriate
AI frameworks, tools, and platforms (e.g., TensorFlow, PyTorch, Databricks, Azure AI) to meet project requirements.

1. Hands-On Development:

• Actively participate in the development of AI models, writing code, building algorithms, and deploying models into production environments.

• Collaborate with data scientists and software engineers to implement AI solutions that are robust, scalable, and efficient.

• Ensure that the technical design is aligned with best practices for AI development, including the use of CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployment (Azure).

1. Technical Leadership:

• Provide technical guidance and mentorship to development teams, ensuring that they follow best practices in AI and software development.

• Review code, design, and architecture to ensure that the solutions meet high standards for quality and security.

• Lead technical discussions in design and implementation phases, making critical decisions that impact the architecture and design of AI solutions.

1. Component and Service Design:

• Architect and design reusable components, microservices, and APIs that can be leveraged across multiple AI projects

• Develop and maintain libraries of reusable code, tools, and templates that accelerate AI development and ensure consistency across projects.

Ensure that all components are designed for integration with existing systems, supporting seamless data flow and interoperability.

1. Research and Innovation:

• Stay up-to-date with the latest advancements in AI, machine learning, deep learning, and cloud computing, bringing new ideas and technologies to the team.

• Experiment with emerging AI technologies, such as Generative AI, Reinforcement Learning, and Neural Architecture Search, to identify their potential applications within KDN.

• Lead the technical exploration of new AI use cases, developing prototypes and proof-of-concept solutions to validate their feasibility.

1. Collaboration and Communication:

• Work closely with stakeholders across KPMG member firms to understand business needs and translate them into technical solutions.

• Communicate complex technical concepts to non-technical stakeholders, ensuring that they understand the capabilities and limitations of AI technologies.

• Collaborate with external partners, including technology providers and academic institutions, to drive innovation and knowledge sharing.

1. Security and Compliance:

• Architect AI solutions with a strong focus on security, ensuring that data privacy and protection are built into the design.

• Implement compliance with industry standards and regulations (e.g., GDPR, ISO 27001), ensuring that AI solutions adhere to KDN's legal and ethical guidelines.

Educational qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.

• Advanced certifications in AI/ML, Cloud Computing, or Enterprise Architecture are highly desirable.

Work experience

• 12+ years of experience in software development, with at least 3 years focused on AI, machine learning, or related technologies.

• Proven experience in architecting and implementing AI solutions, including hands-on development with frameworks like TensorFlow, PyTorch, Keras.

• Extensive experience with cloud platforms, particularly Microsoft Azure, and expertise in deploying AI solutions in cloud environments.

• Strong background in DevOps practices, including CI/CD pipelines, Docker, and Kubernetes.

Skills

• Deep understanding of AI/ML algorithms, model development, and deployment strategies.

• Proficiency in programming languages such as Python with a focus on AI/ML libraries and frameworks.

• Strong problem-solving skills, with the ability to design and implement complex technical solutions.

• Excellent communication skills, with the ability to lead technical discussions and collaborate with cross-functional teams.

• Knowledge of enterprise architecture frameworks (TOGAF) and ITIL practices.