Role : Solutions Architect GenAI, AI/ML & AWS Cloud

Architect the Future of AI with goML

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.

Were looking for a Solutions Architect with deep expertise in designing AI/ML and GenAI architectures on AWS.

In this role, youll be responsible for crafting scalable, high-performance, and cost-effective AI solutions - leveraging AWS AI/ML services, modern data infrastructure, and cloud-native best practices.

If you thrive in architecting intelligent, data-driven systems and love solving complex technical challenges, wed love to hear from you!

Why You? Why Now ?

Generative AI is reshaping industries, and businesses are looking for scalable, cost-efficient, and production-ready AI/ML solutions.

This role is perfect for someone who loves solutioning AI/ML workloads, optimizing cloud infrastructure, and working directly with clients to drive real-world impact.

At goML, you will :

- Own the architecture and solutioning of AI/ML and GenAI applications

- Work with sales & engineering leaders to scope customer needs & build proposals

- Design scalable architectures using AWS services like SageMaker, Bedrock, Lambda, and Redshift

- Influence high-impact AI/ML decisions while working closely with the co-founders

What Youll Do (Key Responsibilities) :

First 30 Days : Foundation & Orientation :

- Deep dive into goMLs AI/ML & GenAI solutions, architecture frameworks, and customer engagements

- Familiarize yourself with goML and AWS partnership workflows

- Work alongside sales leaders to understand customer pain points & proposal workflows

- Review and refine best practices for deploying AI/ML workloads on AWS

- Start contributing to solution architectures, and lead client discussions

First 60 Days : Execution & Impact :

- Own customer AI/ML solutioning, including LLMOps, inference optimization, and MLOps pipelines

- Collaborate with engineering teams to develop reference architectures & POCs for AI -workloads

- Build strategies to optimize AI/ML model deployment, GPU utilization, and cost-efficiency in AWS

- Assist in sizing and optimizing AWS infrastructure for AI/ML-heavy workloads

- Work closely with customers to translate GenAI and AI/ML requirements into scalable architectures

First 180 Days : Ownership & Transformation :

- Lead AI/ML architectural decisions for complex enterprise-scale AI projects

- Optimize multi-cloud and hybrid AI/ML deployments across AWS, Azure, and GCP

- Mentor team members on best practices for GenAI & cloud AI deployments

- Define long-term strategies for AI-driven data platforms, model lifecycle management, and cloud AI acceleration

Represent goML in technical conferences, blogs, and AI/ML meetups

What You Bring (Qualifications & Skills) :

Must-Have :

- 5 - 8 years of experience designing AI/ML and data-driven architectures on AWS

- At least 2 years of hands-on experience in GenAI, LLMOps, or advanced AI/ML workloads

- Deep expertise in AWS AI/ML services (SageMaker, Bedrock, Lambda, Inferentia, Trainium)

- Strong knowledge of AWS Data Services (S3, Redshift, Glue, Lake Formation, DynamoDB)

- Experience in optimizing AI/ML inference, GPU utilization, and MLOps pipelines

- Excellent client-facing communication skills with experience in proposal writing

Nice-to-Have :

- Familiarity with Azure ML, GCP Vertex AI, and NVIDIA AI/ML services

- Experience in LangChain, RAG architectures, and multi-modal AI models

- Knowledge of MLOps automation, CI/CD for AI models, and scaling inference workloads