Remote: Hybrid

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 applications—helping organizations innovate faster, make smarter decisions, and scale AI solutions seamlessly.We’re looking for a highly skilled Technical Architect with deep expertise in AWS, Generative AI, AI/ML, and scalable production-level architectures. In this role, you’ll lead end-to-end AI solution architecture—from 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 ship—we’d 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 prototypes—someone 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 programsdrives best practices in MLOps, DevOps, and cloud securitybrings strong technical leadership and mentoring capabilitiesWhat You’ll Do (Key Responsibilities)First 30 Days: Foundation & Architecture AlignmentDeep dive into goML’s GenAI/AI/ML delivery framework, reference architectures, and deployment standardsUnderstand ongoing customer engagements, solution maturity,



and production constraintsReview current AWS architecture patterns used across projectsAlign with stakeholders on delivery expectations, system SLAs, security requirements, and scalability goalsStart contributing to solution planning, cloud design decisions, and technical estimationFirst 60 Days: Execution & ImpactOwn the architecture of AI/ML and GenAI solutions end-to-end:requirement analysiscloud architecture designimplementation guidancedeployment readinessDesign multi-tenant, enterprise-grade AI systems using AWS services such as:SageMaker, Bedrock, Lambda, API Gateway, DynamoDB, ECS/Fargate, S3, OpenSearch, Step FunctionsImplement best practices for:MLOps + model lifecycleDataOpsDevOps pipelinesDrive Conversational AI / RAG implementations:embeddings & retrieval strategiesvector search + hybrid retrievalinference optimization and cost tuningCollaborate closely with product, engineering, data science, and client teams through architecture reviews and workshopsFirst 180 Days: Ownership & TransformationLead full lifecycle AI architecture—from PoC to production—with reliability and performance focusDesign and guide implementation of:event-driven architecturesserverless & microservices systems for AI workloadsscalable API layers and orchestration flowsEnsure security, compliance,



and governance:IAM + VPC best practicesauditabilitysecurity guardrails and monitoringOwn cost and performance optimization across AI workloads:inference compute optimizationvector database tuningautoscaling strategiesMentor and build strong technical teams:ML engineersPython developerscloud engineersDrive client strategy:roadmapsgo-to-market AI offeringssolution proposals and long-term innovationWhat You Bring (Qualifications & Skills)✅ Must-Have9–12 years of overall experience, with robust background in technical architecture and cloud solutionsProven experience designing and delivering production-grade AI/ML and GenAI applicationsStrong hands-on expertise across AWS services, especially:Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, S3, ECS/Fargate, OpenSearch, RDSDeep knowledge of cloud-native architecture patterns:microservicesevent-driven systemsserverless architectureProven ability to lead technical teams and mentor engineersStrong client-facing skills:requirement gatheringarchitecture walkthroughssolution presentationsstakeholder alignmentExperience managing multiple client engagements or parallel deliveries⭐ Nice-to-HaveExperience with GraphQL API design and advanced enterprise integration patternsExposure to multi-cloud environments (AWS + Azure/GCP)Strong background in building reusable frameworks/platform accelerators for GenAI deliveryCore Technology StackCloud, DevOps & SecurityAWS: Bedrock, SageMaker, Lambda, API Gateway, DynamoDB, S3, ECS, Fargate, OpenSearch, RDSMLOps/DevOps: SageMaker Pipelines, CI/CD (CodePipeline, GitHub Actions), Terraform, AWS CDKSecurity: IAM, VPC, CloudTrail, GuardDuty, KMS, CognitoAI/ML & Generative AILLMs: Bedrock (Claude, Mistral, Titan), OpenAI, LlamaFrameworks: TensorFlow, PyTorch, LangChain, Hugging FaceVector DBs: OpenSearch, Pinecone, FAISSConcepts: RAG pipelines, prompt engineering, fine-tuning, embeddings, inference optimizationArchitecture & ScalabilityServerless + microservices architecturesPerformance optimization & autoscalingEvent-driven systems:SNS, SQS, EventBridge, Step FunctionsAPI design, scalability and resilience engineering

📌 AI Architect (India)
🏢 goML
📍 India