GoML builds and operates production generative AI systems, with over 100 deployments and an AWS Gen AI Competency Partnership. The tech stack—Python, FastAPI/Django, AWS (SageMaker, Bedrock, Lambda), Snowflake, Databricks—reflects a services-delivery model focused on LLM fine-tuning, RAG, and agent systems. Active hiring is senior-skewed engineering roles, concentrated in India, signaling rapid scaling of delivery capacity rather than product development.
Tech Stack 61 technologies
Core StackSelenium Playwright Cypress Postman GitLab Jenkins Python Docker AWS SageMaker AWS Lambda DynamoDB Snowflake Databricks FastAPI Django Flask PostgreSQL MySQL MongoDB pytest REST Assured Bedrock API Gateway AWS Fargate OpenSearch AWS Step Functions Azure Azure OpenAI setuptools+31 more
What GoML Is Building
◆Challenges
- Flawless secure high-performance systems
- Scaling generative ai to enterprise
- Moving beyond experimentation to production
- Designing secure scalable multi-tenant ai solutions
- Modernizing legacy systems
- Ai adoption
- Scaling backend systems for ai
- Reliability of ai/ml platforms
- Scaling ai solutions
- Bridging research and production
▲Active Projects
- Fine-tune pre-trained llms for domain-specific use cases
- Scalable automation frameworks for web, api, data, and backend systems
- Ai-assisted testing approaches
- Automation strategy across multiple engineering teams
- Design generative ai solutions with rag and transformers
- Productionize ml and genai research for enterprise
- Conversational ai / rag implementations
- Multi-tenant enterprise ai system design
- Genai solutions with rag systems
- Llm integration strategies
Hiring Activity
7 roles · 4 in 30d
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