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.
We’re looking for a Solutions Architect with deep expertise in designing AI/ML and GenAI architectures on AWS. In this role, you’ll craft scalable, secure, cost-efficient, and production-ready AI solutions leveraging AWS AI/ML services, modern data infrastructure, and cloud-native best practices. If you thrive on solutioning complex customer problems and love designing intelligent systems at scale—we’d love to hear from you!
Why You? Why Now?
Generative AI is reshaping industries, and enterprises are now actively investing in AI adoption—but they need architectures that are scalable, compliant, cost-effective, and production-ready.
This role is ideal for someone who enjoys:
architecting GenAI + AI/ML workloads
optimizing AWS infrastructure for performance and cost
working directly with customers and leadership teams to deliver real-world impact
At goML, you will:
Own end-to-end architecture for GenAI & AI/ML implementations
Work with sales and engineering leaders to scope needs and craft proposals
Design scalable solutions using AWS services like SageMaker, Bedrock, Lambda, Redshift
Influence high-impact technology decisions while collaborating directly with senior leadership
What You’ll Do (Key Responsibilities)
First 30 Days: Foundation & Orientation
Deep dive into goML’s AI/ML & GenAI solutions, architecture frameworks, and delivery approach
Understand goML’s AWS partnership model and engagement workflows
Partner with sales teams to understand customer pain points, use cases, and proposal flow
Review current best practices for deploying GenAI solutions on AWS
Begin contributing to architecture discussions and leading early-stage client calls
First 60 Days: Execution & Impact
Own customer solutioning for GenAI/AI workloads including:
LLMOps
inference optimization
end-to-end MLOps pipelines
Collaborate with engineering teams to create:
reference architectures
POCs
production blueprints for AI workloads
Optimize AWS deployments for:
GPU utilization
performance tuning
scalability
cost efficiency
Support infrastructure sizing for AI/ML heavy workloads
Translate customer requirements into scalable enterprise-ready cloud architectures
First 180 Days: Ownership & Transformation
Lead architectural decision-making for enterprise-grade GenAI and AI/ML projects
Drive strategies for multi-cloud and hybrid AI deployments across AWS / Azure / GCP
Mentor internal teams on GenAI architecture, AWS best practices, and deployment standards
Define long-term frameworks for:
AI-driven data platforms
model lifecycle management
cloud AI acceleration strategies
Represent goML externally through:
blogs
meetups
technical events and conferences
What You Bring (Qualifications & Skills)
✅ Must-Have
5–8 years of experience designing AI/ML and data-driven architectures on AWS
2+ years hands-on experience in GenAI / LLMOps / advanced AI workloads
Strong expertise in AWS AI/ML services:
SageMaker, Bedrock, Lambda, Inferentia, Trainium
Strong knowledge of AWS Data Services:
S3, Redshift, Glue, Lake Formation, DynamoDB
Experience optimizing:
AI inference performance
GPU utilization
MLOps pipelines
Excellent client-facing communication and presentation skills
Experience in proposal writing / solution documentation
⭐ Nice-to-Have
Familiarity with:
Azure ML
GCP Vertex AI
NVIDIA AI/ML ecosystem
Experience with:
LangChain
RAG architectures
multimodal AI models
Knowledge of:
MLOps automation
CI/CD for AI models
scaling large inference workloads
Why Work With Us?
Remote-first, with offices in Coimbatore for in-person collaboration
Work on cutting-edge GenAI & AI/ML challenges at scale
High impact role influencing enterprise AI solutioning and technical strategy
Competitive salary, leadership growth opportunities, and ESOPs down the line
