AI Lead Software Architect

• A degree in Computer Science, Information Systems, Engineering or related.
• 15+ years - Design and architect scalable AI platforms to develop and deploy AI solutions leveraging ML techniques and Deep Learning Techniques.
• 15+ years - Drive Joint Architecture Design to collaborate with business stakeholders, data scientists, engineering teams, product, and other key partners to gather functional, non-functional requirements for solving AI use case on the AI Platform.
• 15+ years - Architect and build enterprise-grade Agentic AI solutions that combine LLMs, retrieval, structured data access, search, orchestration, governance, compliance, and workflow automation.
• Optimize AI search experiences using platforms such as OpenSearch, and vector databases to support high-relevance, large-scale enterprise applications.
• Python, Spark, OpenSearch, Iceberg, Lakekeeper, ETL Frameworks experience.

• Strong expertise in AWS, Azure, or GCP cloud platforms.
• Proven experience in architecting large-scale data ML/AI solutions.
• Hands-on implementation and leadership experience, driving AI innovation, software application development, ensuring scalability, performance, and data governance.
• Effective communication skills to collaborate with cross-functional teams and stakeholders. Primary customer liaison with oversight of multiple programs.
• Host and deliver demonstrations, inclusive of building prototypes, as required, supporting a variety of customer-driven capabilities demonstrations.
• Proven experience as an AI/ML platform architect.
• Deep understanding of ML algorithms, Deep Learning architecture, models, and frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
• Expertise in hybrid cloud platforms (e.g., AWS, GCP, Azure) and their AI services.
• Strong knowledge of Model development life cycle, software engineering principles, data engineering principles.
• Experience with containerization and orchestration tools onprem and cloud (e.g., AKS, GKE, OpenShift Container Platform, Docker, Kubernetes) for deploying AI/ML models.
• Ability to design and optimize distributed computing systems for AI/ML workloads.
• Familiarity with DevOps practices, CI/CD pipelines, and automation tools in AI-ML contexts.
• Excellent problem-solving skills and ability to address complex technical challenges.

TS/SCI with Full-Scope Polygraph

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