Technical Architect

Job Summary We are looking for an experienced AI Architect to design and drive enterprise-scale AI and Generative AI solutions. The role requires strong hands-on technical expertise combined with architecture leadership across AI/ML, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic AI, cloud platforms, and production AI systems.

The architect will work closely with business, data, application, cloud, security, and engineering teams to translate business requirements into scalable, secure, reliable, and production-ready AI architectures.

Key Responsibilities Define end-to-end architecture for enterprise AI, ML, and Generative AI solutions.

Architect GenAI solutions leveraging LLMs, RAG, vector databases, AI agents, and enterprise data sources.

Evaluate models, AI platforms, frameworks, and architectural patterns based on business and technical requirements.

Establish reusable AI reference architectures, engineering standards, guardrails, and design patterns.

Design scalable approaches for model inference, deployment, evaluation, monitoring, and lifecycle management.

Partner with data architects, cloud architects, security teams, product owners, and engineering teams.

Lead architecture and technical design reviews and guide engineering teams through implementation.

Drive AI security, governance, observability, reliability, and production-readiness considerations.

Provide technical leadership and mentoring to AI/ML engineering teams.

Translate complex business requirements into practical, scalable AI solutions.

Skill Requirements Strong experience in AI/ML solution architecture and enterprise AI system design.

Hands-on expertise with Generative AI, LLMs, RAG, embeddings, vector search, prompt engineering, and AI agents. These technologies are central components of modern AI architecture. [] , [riddam.github.io]
Strong programming experience with Python and AI/ML frameworks.

Experience with PyTorch, Tensor Flow, Hugging Face, MLflow or comparable AI/ML tooling. []
Experience designing RAG architectures, including enterprise data integration and vector databases.

Strong knowledge of MLOps/LLMOps, model deployment, evaluation, monitoring, lifecycle management, and production operations. [] , []
Experience architecting AI solutions on at least one major cloud platform such as Azure, AWS, or Google Cloud.

Strong understanding of REST APIs, microservices, event-driven integrations, and enterprise application integration.

Knowledge of Responsible AI, security, access control, governance, privacy, audit, and model/data lineage. []
Ability to evaluate AI architecture trade-offs across performance, latency, scalability, reliability, security, and cost.

Strong technical leadership and stakeholder communication capabilities.

Key Responsibilities Define end-to-end architecture for enterprise AI, ML, and Generative AI solutions.

Architect GenAI solutions leveraging LLMs, RAG, vector databases, AI agents, and enterprise data sources.

Evaluate models, AI platforms, frameworks, and architectural patterns based on business and technical requirements.

Establish reusable AI reference architectures, engineering standards, guardrails, and design patterns.

Design scalable approaches for model inference, deployment, evaluation, monitoring, and lifecycle management.

Partner with data architects, cloud architects, security teams, product owners, and engineering teams.

Lead architecture and technical design reviews and guide engineering teams through implementation.

Drive AI security, governance, observability, reliability, and production-readiness considerations.

Provide technical leadership and mentoring to AI/ML engineering teams.

Translate complex business requirements into practical, scalable AI solutions.

Other Requirements