TC
TP
TP Conseil
India · On-site Full-time Executive Today
About the role
It looks like you’ve shared the full job posting for the AI Architecture Leader role at TP. How can I assist you with this opportunity?
Here are a few things I can help you with:
| What you might need | How I can help |
|---|---|
| Tailored cover letter | Draft a compelling, role‑specific cover letter that highlights the experience and achievements most relevant to TP’s requirements. |
| Resume / CV refresh | Re‑format and rewrite your resume to showcase the 12‑15 years of experience, architecture leadership, AI/ML expertise, and global delivery impact that TP is looking for. |
| Interview preparation | Create a list of likely interview questions (technical, leadership, and behavioral) and suggest strong, concise answers that align with TP’s culture and the job description. |
| Executive summary / one‑pager | Summarize your qualifications in a concise “elevator pitch” format that you can attach to your application or use in networking conversations. |
| Portfolio / project showcase | Outline how to present past AI‑centric projects (e.g., LLM integration, MLOps pipelines, multi‑region microservice deployments) in a way that demonstrates the impact TP expects. |
| Application checklist | Provide a step‑by‑step checklist to ensure you meet all pre‑employment screening requirements and TP’s submission guidelines. |
| Salary & negotiation guidance | Offer market data and negotiation tips for a senior AI architecture role at a global enterprise. |
Just let me know which of the above (or anything else) you’d like to work on, and I’ll get started right away!
Requirements
- 12–15 years in software or AI engineering, with at least 5 years in a dedicated architecture or delivery leadership role.
- A proven track record of designing and delivering global, enterprise-grade AI/ML or automation solutions.
- Experience governing architecture across distributed teams and multi-product environments.
- Hands-on expertise in AI-first solution design within Azure, AWS, or GCP.
- Deep knowledge of frameworks like TensorFlow, PyTorch, LangChain, and Hugging Face.
- Strong proficiency in LLM integration, Vector Databases, and RAG (Retrieval-Augmented Generation).
- Expertise in microservices, API management, and containerization (Docker, Kubernetes) alongside DevSecOps and CI/CD pipelines.
- Advanced understanding of MLOps pipelines and complex data lifecycle management.
Responsibilities
- Lead the definition of AI architecture blueprints and establish reusable components that align with FAB's global reference models.
- Establish rigorous standards for integration, orchestration, and security across all AI applications and platform layers.
- Partner with Product Managers and Engineering Directors to ensure technical decisions directly support roadmap priorities.
- Direct the architecture for AI implementations across multiple global delivery squads, ensuring high-quality, cost-efficient execution.
- Conduct feasibility assessments and technical validations to de-risk FAB deployments before they hit production.
- Guarantee the scalability, performance, and interoperability of microservices, APIs, and complex data pipelines.
- Integrate advanced AI models (LLMs, NLP, Computer Vision, and Agentic frameworks) into the FAB product suite.
- Define the architectural approach for model serving, observability, and retraining pipelines in collaboration with MLOps.
- Ensure every solution adheres to Responsible AI principles and global standards like GDPR and SOC2.
- Evaluate emerging toolchains and guide the transition toward GenAI and multimodal solutions (e.g., RAG, LangGraph, LLMOps).
- Lead internal workshops to drive architectural best practices and modernization efforts across regional teams.
- Act as a technical diplomat in executive-level discussions, translating complex business needs into elegant technical design options.
Skills
AWSAPI managementAzureCI/CDDockerGCPGenAIHugging FaceKubernetesLangChainLLMLLMOpsMLOpsNLPPyTorchRAGTensorFlowVector Databases
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