AI Solution Architect

We are seeking a AI Solutions Architect with strong experience in designing and developing enterprise-grade AI platforms, RAG pipelines, multi-agent systems, and cloud-native AI solutions. The ideal candidate will be responsible for architecting scalable Generative AI and Agentic AI applications, developing intelligent agent workflows, implementing AI governance and security controls, and delivering production-ready AI solutions across cloud environments. This role requires a strong focus on AI architecture, agent orchestration, retrieval optimization, evaluation, observability, automation, and collaboration with cross-functional teams to deliver secure, scalable, reliable, and high-quality AI solutions.

What Youll Bring

• 12-14 years of experience in software engineering, AI/ML engineering, data engineering, or related fields, with strong hands-on experience building enterprise AI solutions.
• Strong experience designing and developing Generative AI and Agentic AI applications using foundation models, RAG architectures, and agent-based workflows.
• Strong programming and application development experience using Python, FastAPI, and related frameworks.
• Experience designing high-performance RAG pipelines, vector search solutions, embedding workflows, semantic caching, and context-window optimization.
• Strong experience with multi-agent architectures, agent orchestration, memory management, autonomous planning, and stateful workflows.
• Strong understanding of AI evaluation, observability, hallucination reduction, responsible AI, data privacy, governance, and security.
• Ability to collaborate effectively with software engineers, data engineers, data scientists, architects, product teams, and business stakeholders.
• Experience designing scalable, production-ready AI platforms and reusable enterprise architecture patterns.

Preferred Qualification

• Experience with LangChain, LangGraph, and Amazon Bedrock Runtime.
• Experience with Model Context Protocol (MCP) and Agent-to-Agent (A2A) interoperability standards.
• Experience with vector databases such as Milvus and Amazon Aurora PostgreSQL.
• Experience implementing CI/CD pipelines and DevOps practices for AI/ML applications.
• Experience with Docker, Kubernetes, and cloud-native application deployment.
• Knowledge of AI evaluation frameworks, tracing, observability, and monitoring of model and agent performance.
• Experience with AWS services including Amazon Bedrock, Lambda, EKS, SageMaker, S3, RDS, and DocumentDB.
• Knowledge of responsible AI practices including bias mitigation, model risk management, privacy, compliance, and AI safety guardrails.
• Experience optimizing AI applications for latency, scalability, reliability, cost, and token consumption.