Systems Integration Specialist

Role Overview 

We are seeking a highly accomplished DevOps candidate. This role requires 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 

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.