Join a leading global innovator in business and technology services, renowned for technical excellence and impactful innovations. We foster a diverse and inclusive workplace where growth, belonging, and thriving are paramount. As a Senior AI Architect specializing in Private AI, you will play a pivotal role in designing and implementing enterprise-scale AI and GenAI platforms within private cloud, on-premises, and hybrid settings. This position involves architecture, presales, and delivery, focusing on secure, scalable, and sovereign AI solutions leveraging open-source technologies, self-hosted LLMs, and agentic AI systems. You will serve as a key advisor, guiding AI transformation programs while upholding stringent data privacy, governance, and regulatory compliance standards.

Design, build, configure, deploy, and troubleshoot enterprise-grade Private AI/GenAI platforms using open-source and self-hosted technologies. Develop high-level and low-level designs, platform blueprints, deployment patterns, and reference architectures for various AI environments. Implement robust LLM, RAG, and agentic AI solutions, including ingestion pipelines, vector databases, tool integrations, and orchestration components. Build and operationalize AI platforms using Kubernetes, containerization, and modern platform engineering. Implement MLOps/LLMOps frameworks for model training, deployment, monitoring, and governance. Deploy, configure, and optimize self-hosted foundation models and enterprise AI workloads, focusing on GPU utilization, quantization, batching, autoscaling, and latency tuning. Develop accelerators and reusable frameworks for Private AI adoption. Lead RFPs, proposals, and estimations for AI engagements. Advise clients on AI strategy, platform modernization, infrastructure readiness, and AI operating models. Collaborate with cross-functional teams, including infrastructure, cloud, security, and data engineering, to establish enterprise AI foundations. Drive POCs, MVPs, and enterprise AI adoption initiatives.

A minimum of 10 to 13 years of IT experience is required, with over 5 years dedicated to AI/ML architecture. Proven experience in delivering enterprise AI/GenAI solutions across private cloud, on-premises, or hybrid environments is essential. Strong expertise in self-hosted LLMs, open-source AI ecosystems, and enterprise AI platforms is a must. Deep knowledge of LLMs, RAG, vector databases, prompt engineering, and agentic frameworks is expected, along with experience in frameworks like LangChain, LlamaIndex, Haystack, or Semantic Kernel. Familiarity with open-source models such as Llama, Mistral, DeepSeek, Qwen, or Falcon is beneficial. Expertise in the MLOps/LLMOps lifecycle and governance, including model evaluation, hallucination testing, tracing, monitoring, and production support, is crucial. Proficiency in Kubernetes, Docker, OpenShift, platform engineering, and distributed systems is required. Experience with AI infrastructure, including GPUs, model serving frameworks, inference optimization, and vector databases, is necessary. Knowledge of data platforms, data pipelines, and enterprise integration architectures is important. Experience with technologies like MLflow, Kubeflow, KServe, Ray, vLLM, or Ollama is preferred. Understanding of quantization, batching, GPU utilization, latency optimization, autoscaling, and high-availability inference design is essential. Familiarity with GPU sizing, NVIDIA stack, CUDA, MIG, distributed inference/training, storage throughput, networking, capacity planning, workload scheduling, and infrastructure readiness assessment is required. Strong knowledge of security, privacy, and sovereign AI controls, including data isolation, zero-trust architecture, IAM/RBAC, encryption, secrets management, audit logging, secure model hosting, tenant isolation, data residency, and regulatory compliance, is critical. Experience in RFPs, proposals, and estimations, coupled with strong stakeholder management and communication skills, is vital.