AI Architect

Job summary

Own end-to-end AI solution architecture, from use-case discovery and feasibility assessment through production deployment and ongoing monitoring. Govern AI platforms and establish evaluation, security, risk, and responsible AI frameworks while guiding technical teams and communicating architecture decisions to stakeholders.

Job details

Total Experience: 10 & Above years Role Description: • Own end-to-end AI solution architecture from use-case discovery, feasibility assessment, experimentation, production deployment, and ongoing operational monitoring. • Translate business objectives into AI solution designs by defining AI use cases, decision boundaries, architecture patterns, acceptance criteria, and measurable business outcomes. • Design and govern AI platforms and solutions across foundation models, RAG, agent orchestration, data sources, vector search, APIs, cloud infrastructure, security controls, and human-in-the-loop processes. • Establish AI governance, risk, and evaluation frameworks covering model quality, hallucination, groundedness, security, privacy, responsible AI, compliance, performance, cost, and operational readiness. • Provide technical leadership and architecture assurance by guiding engineering and data science teams, reviewing AI implementations, validating solution effectiveness, and communicating architecture decisions, risks, and trade-offs to business and technology stakeholders Required Skill Set: Enterprise AI Solution Architecture Proven experience designing and delivering end-to-end AI, Generative AI, Machine Learning, RAG, and Agentic AI solutions from use-case discovery through production deployment. Generative AI, RAG & Agentic AI Expertise Strong hands-on knowledge of Foundation Models, Prompt Engineering, Embeddings, Vector Databases, Retrieval-Augmented Generation (RAG), Tool Calling, Agent Orchestration, and AI Guardrails. Cloud AI Platforms & Integration Architecture Experience with Azure AI Services, Azure OpenAI, Model Hosting, APIs, Event-Driven Integration, Containers, Identity & Access Management, and scalable cloud-native AI architectures. MLOps / LLMOps, AI Evaluation & Production Operations Experience defining model evaluation frameworks, observability, monitoring, deployment pipelines, versioning, rollback mechanisms, drift detection, and operational readiness for production AI systems. Responsible AI, AI Security & Governance Strong understanding of AI governance, model risk management, privacy engineering, security controls, human-in-the-loop designs, compliance requirements, AI safety, bias/fairness assessment, and responsible AI practices. “Tekshapers is an equal opportunity employer and will consider all applications without regards to race, sex, age, color, religion, national origin, veteran status, disability, sexual orientation, gender identity, genetic information or any characteristic protected by law.” *Disclaimer: This E-Mail may contain Confidential and/or legally privileged Information and is meant for the intended recipient(s) only. If you have received this e-mail in error and are not the intended recipient/s, kindly notify us at itsupport@tekshapers.com and then delete this e-mail immediately from your system. You are also hereby notified that any use, any form of reproduction, dissemination, copying, disclosure, modification, distribution, and/or publication of this e-mail, its contents, or its attachment/s other than by its intended recipient/s is strictly prohibited and may be unlawful. Internet communication cannot be guaranteed to be secured or error-free as information could be delayed, intercepted, corrupted, lost, or contain viruses. Tekshapers. does not accept any liability for any errors, omissions, viruses or computer problems experienced by any recipient as a result of this e-mail.

What you’ll do

Own end-to-end AI solution architecture, from use-case discovery and feasibility assessment through production deployment and ongoing monitoring. Govern AI platforms and establish evaluation, security, risk, and responsible AI frameworks while guiding technical teams and communicating architecture decisions to stakeholders.

Requirements

Requires 10 or more years of experience and proven delivery of end-to-end AI, Generative AI, machine learning, RAG, and agentic AI solutions. Candidates should have hands-on expertise in cloud AI platforms and integrations, production MLOps/LLMOps, AI evaluation, governance, security, privacy, and responsible AI.

Listed skills

• Machine learning · Preferred
• prompt engineering · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Enterprise AI Solution Architecture
• Generative AI
• Machine Learning
• Retrieval-Augmented Generation
• Agentic AI
• Foundation Models
• Prompt Engineering
• Embeddings
• Vector Databases
• Agent Orchestration
• Azure AI Services
• Azure OpenAI
• MLOps
• LLMOps
• Responsible AI
• AI Governance

Job areas

• Technology
• Software
• Data & Analytics
• Management & Leadership

More jobs from Tekshapers