Solution Architect D-L4

What impact will you make?

As the GenAI Solution Architect, you will lead the organization’s strategic vision for GenAI and Agentic AI solutions, driving innovation through advanced AI technologies. Your leadership will shape the development of AI-driven solutions that deliver high value to the business and its customers, positioning the organization as a leader in leveraging Gen AI Solutions.

Work you’ll do

Primary Responsibilities and Daily Tasks

• Design and Implement Agentic AI Architectures: Develop modular, event-driven systems for autonomous agents using modern orchestration tools.
  
• Engineer Agent Workflows and Prompt Strategies: Build multi-step agent workflows using contextual memory, prompt chaining, and tool-based reasoning.
  
• Integrate AI Agents with Enterprise Platforms: Develop and maintain secure integrations with IAM/ITSM tools like ServiceNow, SailPoint, Saviynt, Okta, and Ping.
  
• Optimize Production AI Systems: Tune latency, caching, and inference efficiency in real-world deployments, using tools like LangSmith.
  
• Lead Technical Initiatives and Evaluate Tools: Assess new GenAI tooling, mentor developers, enforce standards, and guide system scalability and fault tolerance.

Short-term and Long-term Goals

• 
  Short-term: Rollout and enhance tailored AI Solution implementations
  
• Long-term: Develop a scalable AI architecture for continuous improvement in AI methodologies, and position the organization as an innovator in domain-specific AI solutions.

Enough about us, let’s talk about you

We are looking for a visionary leader with an advanced understanding of Gen AI, Agent Architectures and cloud technologies, capable of defining AI solutions at an enterprise level.

Essential Skills and Qualifications

• 
  Technical Expertise:
  
• GenAI Tools & Infrastructure: Hands-on with LangGraph, Semantic Kernel, embedding models, fallback handling, and human-in-loop validation.
  
• Agentic AI Frameworks: Expertise in LangGraph, AutoGen, CrewAI, and Python-based orchestration.
  
• Production Deployment: Experience in deploying AI agents with real-time inference, within containerized deployments.
  
• Security & Access Management: Strong understanding of JWT, OAuth, RBAC, and secure API communication.
  
• Performance Monitoring: Skills in using observability tools like LangSmith and implementing tuning mechanisms.
  
• LLMs, Vector Stores & Retrieval Frameworks: Proficiency with Chroma, Weaviate, RAG pipelines, and memory modules.
  
• Leadership and Communication Skills:
  
• Demonstrated ability to lead, mentor, and inspire an engineering team.
  
• Excellent communication skills, with the ability to align technical initiatives with organizational objectives.