Systems Integration Specialist

Role Overview

We are seeking a highly accomplished AI Architect to lead the design, implementation, and governance of enterprise-scale AI platforms integrated with DevOps, Cloud, and Security frameworks.

This role requires a technologist capable of architecting production-grade AI/ML and GenAI solutions while embedding secure DevSecOps practices across multi-cloud environments. The ideal candidate combines deep AI expertise with strong cloud architecture, automation, and security leadership.

Key Responsibilities

AI & Architecture Leadership

• Design and architect enterprise AI/ML and GenAI platforms (LLMs, RAG, agentic AI, AIOps, automation).

• Define scalable AI reference architectures aligned with business objectives.

• Lead end-to-end AI lifecycle management: model development, validation, deployment, monitoring, and governance.

• Establish standards for AI explainability, observability, and ethical AI practices.

Cloud & Platform Engineering

• Architect AI solutions across AWS, Azure, or GCP environments.

• Design scalable data pipelines, model serving infrastructure, and distributed systems.

• Implement containerization (Docker) and orchestration (Kubernetes).

• Build high-availability, resilient AI platforms with performance optimization.

DevOps & MLOps Integration

• Establish CI/CD pipelines for AI/ML workloads.

• Implement Infrastructure as Code (Terraform, ARM, CloudFormation).

• Build automated model deployment and monitoring pipelines (MLOps).

• Integrate AI into DevSecOps frameworks for secure continuous delivery.

Security & Governance

• Architect secure AI systems adhering to Zero Trust principles.

• Implement model security controls (data protection, encryption, access control, secrets management).

• Conduct threat modeling for AI workloads (prompt injection, model poisoning, drift).

• Ensure compliance with enterprise security, regulatory, and audit requirements.

• Collaborate with security teams to perform red-teaming and AI risk assessments.

Stakeholder & Strategic Leadership

• Work closely with stakeholders to define AI transformation roadmaps.

• Provide architectural governance and technical mentorship to engineering teams.

• Evaluate emerging AI technologies and define adoption strategies.

• Drive innovation initiatives aligned with enterprise modernization goals.

Required Qualifications

• 3+ years of experience in enterprise architecture, cloud engineering, or platform leadership.

• 3+years designing and deploying AI/ML or GenAI solutions in production.

• Strong expertise in Python, AI frameworks (TensorFlow, PyTorch, LangChain, etc.).

• Deep understanding of LLM architecture, RAG systems, and agentic frameworks.

• Hands-on experience with Kubernetes, Docker, CI/CD pipelines.

• Strong cloud architecture experience (AWS/Azure/GCP certifications preferred).

• Experience implementing DevSecOps practices.

• Strong knowledge of enterprise security frameworks and cloud security controls.

• Experience designing high-availability distributed systems.

Preferred Qualifications

• Experience building enterprise AI platforms (AIOps, self-healing systems, automation).

• Knowledge of data governance and enterprise knowledge graphs.

• Experience integrating AI with ITSM platforms (ServiceNow, Remedy).

• Cloud or Security certifications (AWS/Azure Architect, CISSP, CCSP, etc.).

• Experience leading global, cross-functional technical teams.

Leadership Competencies

• Strategic thinking and enterprise vision.

• Strong executive communication skills.

• Ability to translate complex AI concepts into business value.

• Governance-driven mindset with innovation orientation.