AI Architect - AWS Hybrid AI Platform, Security and LLMOps

Who We Are

We operate beyond organizational silos, combining deep expertise in data engineering and AI-driven solutions to design and deliver scalable, user-centric applications and modern data architectures. We support a wide range of industries, including Automotive, Banking, Insurance, Telecommunications, E-commerce, and Public Services.

We are looking for a hands-on AI Architect - Lead Consultant to design and govern secure, production-grade AI platform and solution patterns across an AWS-centered hybrid ecosystem. The role focuses on platform integration, runtime patterns, private connectivity, identity, model gateways, managed and open-source models, LLMOps/MLOps, observability, reliability, cost, security and responsible AI, with approximately 70% hands-on technical work and 30% architecture leadership.

What you'll be doing

• Own reusable platform and solution architecture patterns for AI applications, agents, models, retrieval services, data flows, APIs and hybrid infrastructure.
• Architect containerized, serverless, event-driven and private model-serving workloads using Kubernetes, EKS/EKS Anywhere, ECS, Lambda and approved runtime platforms.
• Design secure cloud-to-on-premises connectivity using Direct Connect, Storage Gateway, enterprise networking, private endpoints, DNS, proxies and reverse-proxy patterns.
• Design identity and access controls using AWS IAM, Secrets Manager, Active Directory integration, workload identities, least privilege, encryption, certificates and audit logging.
• Define model-gateway, model-routing, open-source model, managed-model and private inference patterns, including safety, tenancy, quotas and cost controls.
• Establish infrastructure automation, environment promotion, CI/CD, policy and versioning using CloudFormation, Terraform and approved DevOps practices.
• Establish observability, service-level objectives, incident response and rollback using logs, metrics, tracing, quality signals and cost telemetry.
• Define LLMOps/MLOps controls for automated evaluation, release gates, monitoring, drift, retraining, reliability and operational support.
• Build reference deployments, diagnose platform issues, review infrastructure as code and runtime configurations, and mentor architects and engineers.

What you'll bring along

• At least 10 years of relevant technology experience preferred; candidates with 8 years may be considered where platform depth is exceptional.
• At least 4 years designing and delivering production AI/ML, cloud, data or platform solutions.
• At least 5 years of software, cloud, platform, infrastructure, DevOps or security engineering experience.
• At least 2 years architecting AI solutions across cloud and private, hybrid or on-premises deployment models.
• Hands-on experience with AWS compute, containers, serverless, networking, security, observability and infrastructure automation services.
• Experience with Kubernetes, APIs, IAM, secrets, private connectivity, CI/CD, infrastructure as code, monitoring, incident response and rollback.
• Working knowledge of foundation models, RAG, agents, model gateways, open-source model serving, evaluation and AI governance.
• Ability to review code, API contracts, infrastructure as code, runtime configurations, network flows, security designs and operational telemetry.
• Strong communication, influence, mentoring and judgment under ambiguity; experience with regulated environments and relevant certifications is considered a plus.
• Excellent command of English, both written and spoken