Solution Architect

CES has 26+ years of experience in delivering Software Product Development, Quality Engineering, and Digital Transformation Consulting Services to Global SMEs & Large Enterprises. CES has been delivering services to some of the leading Fortune 500 Companies including Automotive, AgTech, Bio Science, EdTech, FinTech, Manufacturing, Online Retailers, and Investment Banks. These are long-term relationships of more than 10 years and are nurtured by not only our commitment to timely delivery of quality services but also due to our investments and innovations in their technology roadmap. As an organization, we are in an exponential growth phase with a consistent focus on continuous improvement, process-oriented culture, and a true partnership mindset with our customers. We are looking for the right qualified and committed individuals to play an exceptional role as well as to support our accelerated growth. You can learn more about us at:
Job Overview:
The AI Solution Architect and CoE Lead will play a pivotal role in leading the design, development, and implementation of artificial intelligence solutions within the organization. This position will also be responsible for driving the strategy for AI excellence and best practices by establishing and overseeing the AI Centre of Excellence (CoE). The CoE will focus on nurturing AI talent, fostering knowledge sharing, and continuously evolving AI practices across the organization.
Key Responsibilities:
1. AI Solution Architecture:

• Design and Develop AI Solutions: Lead the end-to-end process of designing, developing, and deploying AI solutions tailored to business needs.

• Technical Leadership: Provide technical leadership to cross-functional teams working on AI-related projects, ensuring high standards in solution design, integration, and deployment.

• Consulting and Advisory: Work closely with stakeholders to identify business requirements and translate them into AI-powered solutions, including machine learning models, data pipelines, and AI-driven processes.

• Platform Selection and Integration: Evaluate and select appropriate AI tools, platforms, and technologies to meet business goals. Oversee integration with existing systems, ensuring scalability and efficiency.

• Optimization and Innovation: Continuously monitor, optimize, and evolve AI solutions, keeping the organization at the forefront of AI advancements.
2. Centre of Excellence (CoE) Management:

• CoE Strategy Development: Develop and implement a strategy for the AI Centre of Excellence, ensuring alignment with business objectives and AI best practices.

• Knowledge Sharing and Governance: Establish frameworks for knowledge sharing, training, and governance, ensuring that AI practices are consistent and scalable across the organization.

• Innovation Culture: Foster a culture of innovation and experimentation, encouraging cross-functional collaboration and new AI research and application.

• Talent Development: Lead efforts to upskill internal teams by organizing training sessions, workshops, and seminars focused on the latest AI technologies and methodologies.

• Standardization and Best Practices: Define AI-related standards, processes, and best practices across the organization. Ensure all teams adhere to these guidelines to maintain quality and consistency.
3. Stakeholder Engagement:

• Cross-Functional Collaboration: Collaborate with business leaders, data scientists, IT teams, and product managers to deliver effective AI solutions.

• Client-facing Engagement: Engage with clients to understand their needs, demonstrate AI capabilities, and provide thought leadership on how AI can address their challenges.

• Executive Reporting: Regularly report to senior leadership on the progress of AI initiatives, highlighting key milestones, risks, and opportunities.
4. Research and Development:

• Emerging Technologies: Stay updated on the latest developments in AI technologies, including deep learning, reinforcement learning, natural language processing (NLP), and computer vision, and evaluate their potential impact on business processes.

• Proof of Concept (PoC): Lead the development of PoCs and pilot projects to test AI ideas and validate their feasibility before broader implementation.
5. AI Governance and Compliance:

• Ethical AI: Ensure the responsible and ethical use of AI, taking into consideration issues related to fairness, transparency, privacy, and security.

• Compliance: Maintain awareness of AI-related regulations and ensure the organization's solutions adhere to legal, ethical, and industry standards.
Qualifications:
Education:

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, AI, or a related field. A Ph.D. in AI or related fields is a plus.
Experience:

• 11+ years of experience in AI, machine learning, or data science, with a proven track record of delivering AI solutions.

• 7+ years of experience in a leadership or architecture role, ideally with some experience in leading a Centre of Excellence or a similar initiative.

• Hands-on experience with AI frameworks such as TensorFlow, PyTorch, Scikit-learn, and cloud platforms like AWS, Azure, or Google Cloud.

• Experience in multiple industries is advantageous (e.g., healthcare, finance, retail).
Skills:

• AI/ML Expertise: Strong understanding of machine learning algorithms, deep learning, natural language processing, computer vision, and data-driven problem-solving techniques.

• Architecture Skills: Proven ability to design and architect scalable, reliable, and high-performance AI solutions.

• Leadership and Communication: Excellent leadership skills with the ability to influence and collaborate with cross-functional teams. Strong presentation and communication skills for engaging stakeholders at all levels.

• Project Management: Experience managing large, complex projects with diverse teams and tight deadlines.

• Governance and Best Practices: Deep understanding of AI governance frameworks, industry standards, and ethical guidelines.
Certifications (Optional):

• Certified AI Professional (e.g., Microsoft Certified: Azure AI Engineer, AWS Certified Machine Learning).

• Certified Solutions Architect (e.g., AWS Solutions Architect, Google Professional Cloud Architect).
Preferred Traits:

• Visionary Thinker: Ability to foresee AI trends and leverage them strategically for business growth.

• Problem Solver: Strong analytical skills and an innovative mindset to solve complex business problems using AI.

• Mentor: Passion for mentoring and developing the next generation of AI talent.
Why CES?

• Flexible working hours to create a work-life balance.
• Opportunity to work on advanced tools and technologies.
• Global exposure to not only collaborate with the team, but also to connect with the client portfolio and build professional relationships.
• Highly encouraged for any innovative ideas & thoughts and we support in executing the same.
• Periodical and on-spot rewards and recognitions on your performance.
• Provides a better platform for enhancing skills via many different L&D programs.
• Enabling and empowering atmosphere to work along.

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