AI ML Architect

At goML, we design and build cutting-edge Generative AI, AI/ML, and Data Engineering solutions that help businesses unlock the full potential of their data, drive intelligent automation, and create transformative AI-powered experiences. Our mission is to bridge the gap between state-of-the-art AI research and real-world enterprise applicationshelping organizations innovate faster, make smarter decisions, and scale AI solutions seamlessly. Were looking for a highly skilled Technical Architect with deep expertise in AWS, Generative AI, AI/ML, and scalable production-level architectures. In this role, youll lead end-to-end AI solution architecturefrom PoC to enterprise-scale production, drive cloud security and scalability best practices, and work closely with multiple clients and internal delivery teams. If you love architecting robust systems, mentoring engineering teams, and building GenAI solutions that actually shipwed love to hear from you. That requires architects who can think beyond models and prototypessomeone who can design secure, scalable, multi-tenant AI solutions with clear MLOps foundations and cloud-native best practices. owns architectures end-to-end (not just diagrams) drives best practices in MLOps, DevOps, and cloud security First 30 Days: Foundation & Architecture Alignment Deep dive into goMLs GenAI/AI/ML delivery framework, reference architectures, and deployment standards Review current AWS architecture patterns used across projects Start contributing to solution planning, cloud design decisions, and technical estimation Own the architecture of AI/ML and GenAI solutions end-to-end: requirement analysis cloud architecture design implementation guidance deployment readiness Design multi-tenant, enterprise-grade AI systems using AWS services such as: DevOps pipelines Drive Conversational AI / RAG implementations: vector search + hybrid retrieval Collaborate closely with product, engineering, data science, and client teams through architecture reviews and workshops First 180 Days: Ownership & Transformation Lead full lifecycle AI architecturefrom PoC to productionwith reliability and performance focus Design and guide implementation of: event-driven architectures serverless & microservices systems for AI workloads security guardrails and monitoring Own cost and performance optimization across AI workloads: vector database tuning ML engineers Python developers cloud engineers go-to-market AI offerings solution proposals and long-term innovation 912 years of overall experience, with strong background in technical architecture and cloud solutions Proven experience designing and delivering production-grade AI/ML and GenAI applicationsStrong hands-on expertise across AWS services, especially:Deep knowledge of cloud-native architecture patterns:serverless architecturearchitecture walkthroughsExperience managing multiple client engagements or parallel deliveries Experience with GraphQL API design and advanced enterprise integration patterns Exposure to multi-cloud environments (AWS + Azure/GCP) Strong background in building reusable frameworks/platform accelerators for GenAI delivery Cloud, DevOps & Security AWS: Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, S3, ECS, Fargate, OpenSearch, RDS MLOps/DevOps: SageMaker Pipelines, CI/CD (CodePipeline, GitHub Actions), Terraform, AWS CDK AI/ML & Generative AI Vector DBs: OpenSearch, Pinecone, FAISS Architecture & Scalability Serverless + microservices architectures Performance optimization & autoscaling .