Consulting Point
AI Platform Engineer
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Senior / Lead AI Platform Engineer (Consulting)
Are you a cloud-native engineering expert who wants to build the foundation for the future of Enterprise AI?
We are a leading global business and technology transformation consultancy, and we are looking for a Senior / Lead AI Platform Engineer to join our dynamic digital innovation practice. In this role, you will design, build, and operate the critical infrastructure beneath LLMs, autonomous agents, and MLOps pipelines for major enterprise clients.
This is a client-facing consulting role that combines cutting-edge technical architecture with strategic delivery.
The Role & Responsibilities
As a Senior Consultant, you will act as a technical lead across hybrid and multi-cloud environments to safely scale AI infrastructure.
- Architect AI Infrastructure: Deploy GPU-accelerated compute, Kubernetes/OpenShift, and cloud-native services across cloud and on-premises environments.
- Build Core Platform Components: Develop model serving infrastructure (vLLM/LiteLLM), agent orchestration frameworks (LangGraph/MCP), evaluation systems, and guardrail layers.
- Drive DevOps & MLOps: Implement robust LLMOps pipelines, monitoring, and fine-tuning capabilities using Infrastructure as Code (Terraform) and GitOps.
- Consult & Lead: Lead client workshops, architecture reviews, and technical briefings while managing risk and project timelines.
- Grow the Business & Practice: Contribute to RFPs/bids, mentor junior engineers, publish whitepapers, and help shape our core AI platform offerings.
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Your Profile
We are looking for deep, hands-on engineering experience. You do not need to tick every box, but you should bring strength in cloud-native systems and AI platforms.
- Security Clearance: Must hold active SC Clearance (or be eligible and willing to undergo the vetting process).
- AI & GenAI Tooling: Experience with model gateways, agent orchestration frameworks, evaluation engineering, and AI-observability tools (e.g., NeMo Guardrails, LangSmith, OpenTelemetry).
- MLOps Platforms: Hands-on experience with Azure ML, Databricks, or SageMaker, alongside vector databases (Pinecone, Milvus, pgvector).
- Cloud-Native Expertise: 5+ years of experience across AWS, Azure, or GCP with deep Kubernetes expertise (AKS, EKS, GKE, or OpenShift).
- Consulting Skills: Proven ability to lead technical delivery, manage stakeholders, and run architecture workshops.
- Core Tech Stack: Strong Python scripting skills (plus Go, Rust, or Bash) and standard DevOps/IaC tooling. Experience in regulated industries (Financial Services/Insurance) is a major plus.
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What We Offer
- A highly collaborative community valuing continuous learning and rapid experimentation.
- Structured training, funding for certifications, and clear pathways into leadership.
- Opportunities to shape a rapidly growing AI engineering culture from the ground up.
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