AI Architect (Alappuzha)

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. Why You Why Now Enterprises are moving beyond experimentation and pushing GenAI into real production systems. 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. This role is ideal for a leader who: owns architectures end-to-end (not just diagrams) can manage multiple clients / multiple programs drives best practices in MLOps, DevOps, and cloud security brings strong technical leadership and mentoring capabilities What Youll Do (Key Responsibilities) First 30 Days: Foundation & Architecture Alignment Deep dive into goMLs GenAI/AI/ML delivery framework, reference architectures, and deployment standards Understand ongoing customer engagements, solution maturity, and production constraints Review current AWS architecture patterns used across projects Align with stakeholders on delivery expectations, system SLAs, security requirements, and scalability goals Start contributing to solution planning, cloud design decisions, and technical estimation First 60 Days: Execution & Impact 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: SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, ECS/Fargate, S3, OpenSearch, Step Functions Implement best practices for: MLOps + model lifecycle DataOps DevOps pipelines Drive Conversational AI / RAG implementations: embeddings & retrieval strategies vector search + hybrid retrieval inference optimization and cost tuning 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 scalable API layers and orchestration flows Ensure security, compliance, and governance: IAM + VPC best practices auditability security guardrails and monitoring Own cost and performance optimization across AI workloads: inference compute optimization vector database tuning autoscaling strategies Mentor and build strong technical teams: ML engineers Python developers cloud engineers Drive client strategy: roadmaps go-to-market AI offerings solution proposals and long-term innovation What You Bring (Qualifications & Skills) Must-Have 912 years of overall experience, with robust background in technical architecture and cloud solutions Proven experience designing and delivering production-grade AI/ML and GenAI applications Strong hands-on expertise across AWS services, especially: Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, S3, ECS/Fargate, OpenSearch, RDS Deep knowledge of cloud-native architecture patterns: microservices event-driven systems serverless architecture Proven ability to lead technical teams and mentor engineers Strong client-facing skills: requirement gathering architecture walkthroughs solution presentations stakeholder alignment Experience managing multiple client engagements or parallel deliveries Nice-to-Have 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 Core Technology Stack 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 Security: IAM, VPC, CloudTrail, GuardDuty, KMS, Cognito AI/ML & Generative AI .