GenAI Architect - SM

The Generative AI Technology Lead will define and execute the organization's vision and strategy for generative AI, driving innovation, architectural excellence, and the integration of advanced AI solutions to meet business objectives. This role combines strategic oversight, hands-on technical expertise, and leadership in AI initiatives.

Key Responsibilities:

• Define and oversee the organization's generative AI vision, ensuring alignment with business goals.
• Engage with senior leadership to develop and refine the company's generative AI roadmap.
• Evaluate and integrate cutting-edge generative AI frameworks, models, and methodologies.
• Lead the design, architecture, and deployment of large-scale AI models and algorithms.
• Ensure rigorous standards in data practices, model development, and algorithm performance.
• Mentor technical teams and foster a culture of innovation and professional development.
• Communicate complex AI concepts and project updates to both technical and non-technical stakeholders.
• Identify and mitigate risks, ensuring ethical, compliant, and secure AI practices.
• Develop and refine prompts for LLMs and design use cases tailored to business needs.
• Integrate AI services (Azure, AWS, Google Cloud, OpenAI) into existing systems for automation and workflow optimization.
• Stay updated on NLP, LLM, and generative AI advancements and implement innovative methodologies.

Key Skills:

Generative AI Algorithms, LLMs & Prompt Engineering, GenAI Frameworks (LangChain, LlamaIndex), Cloud Platforms (AWS/Azure/GCP), Microservices Architecture, AI Integration Automation

Function:

Technical Expertise:

• Programming & Tools:

  Python, Java 11+, .NET, Node.js, React, Angular

• Applications & Modernization:

  Cloud-native application development, integration design patterns, application modernization

• Architecture & DevOps:

  Microservices, event-driven architectures, DevOps tools (Jenkins, Git, Azure DevOps, SonarQube), SRE practices

• Data & Platforms:

  Hibernate, Spring Data, MongoDB, Cassandra, DynamoDB, Elasticsearch, Neo4J, Redis, Hadoop

• AI & GenAI Specifics:

  LLMOps, RAG patterns, AI Guardrails, Code Companions (GHCP, Amazon Q, Tabnine, cursor.ai, IBM WatsonX), AI strategies, prompt design, and deployment

Desired Competencies:

• Strong problem-solving, analytical, and strategic thinking skills
• Ability to mentor and guide technical teams in advanced AI technologies
• Experience in architectural decisions, cloud adoption, and application modernization
• Hands-on expertise with GenAI models, frameworks, and integration into business processes