Role Summary
We have an excellent opportunity for an experienced AWS Artificial Intelligence (AI)/Machine Learning (ML) Architect to join our AWS services team and play a key role in supporting our continued growth plans. These plans aim to solidify our position as a leading global Services partner with AWS. The AWS AI/ML Architect will serve as the Subject Matter Expert (SME) in assisting end-customers to design and build machine learning solutions using the AWS AI and ML stack to address complex business challenges. Additionally, the role involves creating white papers, blogs, reference implementations, labs, and presentations to promote AWS AI/ML design patterns and best practices for various customer scenarios. The architect will also mentor and educate the broader AWS technical team, enhancing their ability to integrate the AI/ML stack into customer architectures and scenarios. This role entails hands-on work with machine learning, linking technology to measurable business value, and requires strategic thinking about business, products, and technical challenges. A typical AWS AI/ML Architect will have had five or more years experience in a technical consulting type role, with an excellent understanding and experience of delivering AWS centric solutions, specifically focused on AI and ML.
Key Duties and Responsibilities
Working in close collaboration with the local (in-country) AWS sales and technical team, along with the local Regional Service Centre, the core responsibilities of the role include, but not limited to, the following:
Project Delivery
- Work with end-customer's AI team to deeply understand their business and technical needs and build AI solutions that make the best use of the AWS Cloud platform and AI/ML services
- Undertake individual consultancy assignments or work on a project as part of a larger team analysing customer requirements, gathering and analysing data and recommending solutions
- Technically manage the assessment, design, and implementation of solutions
- Ensure consultancy assignments are undertaken consistently and with quality
- Highlight technical risks so that any Ingram Micro exposure to commercial loss can be minimised
- Produce and update assignment documentation as required
- Ensure that hand over to relevant support organisation is successfully completed
- Provide technical support as requested, for internal and external customers
- Lead and present at customer opportunities and workshop sessions
- Design and implement scalable, secure, and high-performance AI architectures on AWS.
- Develop and deploy machine learning models and AI solutions.
- Ensure AI architecture aligns with business requirements and industry best practices.
Pre-Sales, Scoping, and Services Development
- Lead, or be part of, customer scoping calls or workshop sessions
- Lead and contribute to Statement of Works
- Build and maintain a good awareness of the internal SMEs areas of specialism
- Educate partners and end-customers on the value proposition of AWS, and participate in deep architectural discussions to ensure solutions are designed for successful deployment in the cloud
- Identify other Ingram Micro service and sales opportunities
- Evangelize AWS Services and share best practices through forums such as AWS blogs, whitepapers, reference architectures and public-speaking events
- Support the development of the regional AWS services propositions, solutions and GTM
- Work with thought leaders within Ingram Micro to allow for continued success and to help identify areas of potential growth Lead with qualification of designs and opportunities
- Develop proof of concepts (POCs) to showcase the capabilities of proposed AI solutions.
Personal Skills Development
- An active and contributing member of our regional AWS technical community
- Build and maintain a strong relationship with the channel partners internal technical team
- Keep up-to-date with current and future technologies, products and strategies
- Build a solid skills foundation in chosen subject matter
- Maintain relevant vendor certifications
- Build and enhance relationships with peers
- Continue development of consultative skills
Qualifications and Experience
An AWS AI/ML Architect should also have the following qualifications and experience:
Desirable Qualifications
- AWS Certified Machine Learning - Specialty certification.
Expected Experience (Design, Deployment and Transformation)
- 3+ years on AWS complex cloud environments
- Relevant experience in machine learning model development lifecycle including (but not limited to) training, fine tuning feature engineering techniques and deployment options
- Significant hands-on experience with Python, R or other programming languages and independently building prototype applications
- Significant experience building with libraries like PyTorch, Tensorflow, MxNet and ScikitLearn
- Experience with deep learning and neural networks.
- Familiarity with data visualization tools such as Tableau or QuickSight.
- Knowledge of data security and compliance regulations.
- Proven experience with AWS AI/ML services such as SageMaker, Rekognition, Comprehend, and Lex.
Knowledge, Skills, and Characteristics
- Five or more years of experience in a technical consulting and business analyst type role
- Excellent communicator both verbally and written (both local and English)
- Experienced, mature, influential, assertive and diplomatic
- Able to operate independently or as part of a larger team
- Able to technically manage other people on large scale projects
- Able to network with industry peers and customers
- Industry leading knowledge of chosen subject matter
- A flexible approach to work and prepared go the extra mile to exceed customer expectations
- Applies knowledge and skills through handling complex problems beyond own area of expertise
- Ability and willingness to travel
