Solutions Architect

Role: AI Solution Architect I

Section Title: Job Summary

Job Summary

The Mid-Level AI Architect is responsible for designing and implementing AI and cloud architectures, with a focus on integrating generative AI technologies. This role requires a solid understanding of AI and cloud platforms and the ability to engage with customers on various architectural topics in cloud and data center environments. The ideal candidate is passionate about GenAI and AI technologies, keeps up with industry trends, and drives innovation within the organization and for clients. As a key contributor, you will interact frequently with customers, provide expert opinions, and support HCL's strategic vision.

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Section Title: Key Responsibilities

Key Responsibilities

Key Responsibilities Technical & Engineering Leadership • Design and implement AI and cloud architectures, integrating GenAI technologies to enhance functionality and scalability. • Participate in architectural discussions with clients, providing guidance on best practices for AI and cloud integration. • Ensure solutions align with microservice and container-based environments across public, private, and hybrid clouds. • Contribute to HCL's thought leadership in the Cloud Native domain with a solid understanding of open source technologies (e.g., Kubernetes/CNCF) and partner technologies. • Collaborate on technical projects with global partners, including Google, Microsoft, AWS, IBM, Red Hat, Intel, Cisco, and Dell/VMware. Service Delivery & Innovation • Develop GenAI solutions from ideation to MVP, ensuring high performance and reliability within cloud native frameworks. • Optimize AI and cloud architectures to meet client requirements, balancing efficiency and effectiveness. • Evaluate existing complex solutions and recommend architectural improvements to transform applications with cloud-native/12-factor characteristics. • Promote the adoption of GenAI technologies within cloud-native projects, driving initiatives that push the boundaries of AI integration in cloud services.

Thought Leadership and Client Engagement • Provide architectural guidance to clients on incorporating GenAI and machine learning into their cloud native applications and architectures. • Conduct workshops, briefings, and strategic dialogues to educate clients on AI benefits and applications, building strong, trust-based relationships. • Act as a trusted advisor, contributing to technical projects (PoCs and MVPs) with a focus on technical excellence and on-time delivery. • Author whitepapers, blogs, and speak at industry events, maintaining a visible presence as a thought leader in AI and cloud architecture. • Create and record videos to share insights and opinions on AI and cloud technologies, enhancing HCL's industry leadership. Collaboration and Multi-Customer Management • Engage with multiple customers simultaneously, providing high-impact architectural consultations and fostering strong relationships. • Work closely with internal teams and global partners to ensure seamless collaboration and knowledge sharing across projects. • Maintain a hands-on technical credibility, staying current with industry trends and mentoring others in the organization.

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Section Title: Skill Requirements

Skill Requirements

Mandatory Skills & Experience • Experience: 5+ years in cloud and AI architecture design, 3+ years in software development. • Technologies: Proficiency in Python, Java (and/or Golang), and Spring; expertise in AWS, Azure, Google Cloud; Kubernetes and containerization. • AI Expertise: Machine learning model development, GenAI models (e.g., GPT, BERT, DALL-E, GEMINI), NLP techniques. • Big Data: Experience with Hadoop and Spark. • Ethics: Knowledge of AI ethics and governance. • Methodologies: Agile and Scrum project management. Desired Skills & Experience • API Development and Integration • Data Storage Solutions (SQL, NoSQL) • AI Model Optimization and Scaling; Model Evaluation and Validation 2 • Monitoring and Logging (Prometheus, ELK Stack) • GenAI Model Utilization and MLOps • Proficiency in Data Engineering for AI, including preprocessing, feature engineering, and pipeline creation • Expertise in AI Model Fine-Tuning, Evaluation, and Bias Mitigation • Knowledge of Serverless Computing, Distributed Systems, Deep Learning Frameworks (TensorFlow, PyTorch), and Emerging Technology Trends Verifiable Certification • At least one recognized cloud professional certification (AWS Certified Solutions Architect, Google Professional Cloud Architect, or Microsoft Certified: Azure Solutions Architect Expert)

Soft Skills and Behavioural Competencies • Excellent communication and teamwork skills, capable of collaborating effectively with diverse teams. • Demonstrates a strong customer orientation and innovative problem-solving abilities. • Skilled at adapting to organizational changes and contributing to a culture of innovation.

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Section Title: Must Have Skills

Must Have Skills

Python