Data Architect

About the Role

The Solution Designer/Tech Lead will be a key technical advisor, collaborating with onshore teams and leadership to deliver high-impact Data and AI/ML projects.

Responsibilities

• Design and architect Generative AI solutions leveraging AWS services such as Bedrock, S3, PG Vector, Kendra, and SageMaker.
• Collaborate closely with developers to implement solutions, providing technical guidance and support throughout the development lifecycle.
• Lead the resolution of complex technical issues and challenges in AI/ML projects.
• Conduct thorough solution reviews and ensure adherence to best practices and company standards.
• Navigate governance processes and obtain necessary approvals for initiatives.
• Make critical architectural and design decisions aligned with organizational policies and industry best practices.
• Liaise with onshore technical teams, presenting solutions and providing expert analysis on proposed approaches.
• Conduct technical sessions and knowledge-sharing workshops on AI/ML technologies and AWS services.
• Evaluate and integrate emerging technologies and frameworks like LangChain into solution designs.
• Develop and maintain technical documentation, including architecture diagrams and design specifications.
• Mentor junior team members and foster a culture of innovation and continuous learning.
• Collaborate with data scientists and analysts to ensure optimal use of data in AI/ML solutions.
• Coordinate with clients, data users, and key stakeholders to achieve long-term objectives for data architecture.
• Stay updated on the latest trends and advancements in AI/ML and cloud and data technologies.

Key Qualifications and experience:

• Extensive experience (14-18 years) in software development and architecture, with a focus on AI/ML solutions.
• Deep understanding of AWS services, particularly those related to

  AI/ML (Bedrock, SageMaker, Kendra, etc.).

• Proven track record in designing and implementing data, analytics, repor

  ting and/or AI/ML solutions.
• Strong knowledge of data structures, algorithms, and software design patterns.
• Expertise in data management, analytics, and reporting tools.
• Proficiency in at least one programming language commonly used in AI/ML (e.g., Python, Java, Scala).
• Familiarity with DevOps practices and CI/CD pipelines.
• Understanding of AI ethics, bias mitigation, and responsible AI principles.
• Basic understanding of data pipelines and ETL processes, with the ability to design and implement efficient data flows for AI/ML models.
• Experience in working with diverse data types (structured, unstructured, and semi-structured) and ability to preprocess and transform data for use in generative AI applications.