Description

Define end-to-end architecture for AI, ML, Generative AI, RAG, and agentic AI solutions. Select suitable models, frameworks, cloud services, databases, orchestration tools, and deployment patterns. Design scalable and secure integration architectures across enterprise systems, APIs, data sources, and business workflows. Establish architecture standards for LLMOps, MLOps, model serving, monitoring, data governance, and responsible AI. Define security controls for AI systems, including identity and access management, encryption, data privacy, prompt injection protection, and auditability. Create reference architectures, technical roadmaps, architecture diagrams, integration specifications, and technology recommendations. Evaluate technology feasibility, cost, latency, reliability, scalability, and operational risk before implementation. Work closely with engineering, data, product, security, and client stakeholders to align architecture with business goals. Review solution designs and technical implementations to ensure compliance with defined standards. Guide teams on architecture decisions, technical trade-offs, enterprise integration, and AI governance.

Skill and Ability:

6+ years of experience in software engineering, cloud engineering, solution architecture, AI/ML engineering, or related roles. 2+ years of experience designing AI, ML, GenAI, RAG, or LLM-powered production solutions. Strong understanding of cloud architecture across AWS, GCP, or Azure. Experience designing distributed systems, APIs, microservices, data pipelines, event-driven systems, databases, queues, and caching layers. Hands-on understanding of LLMs, RAG, embeddings, vector databases, model deployment, and agentic AI patterns. Strong knowledge of enterprise integration, data security, governance, scalability, observability, and high-availability principles. Ability to communicate architecture decisions clearly to business stakeholders, clients, and engineering teams. Experience producing solution architecture documents, technical proposals, diagrams, and implementation roadmaps. Cloud architecture certifications in AWS, GCP, or Azure. Experience with Docker, Kubernetes, Terraform, and infrastructure-as-code practices. Knowledge of AI safety, responsible AI, model governance, privacy, and compliance requirements. Experience leading client workshops, technical discovery sessions, or architecture reviews

Key Skills

Education

Bachelor's degree