Solutions Architect

About Geta.ai

Geta.ai builds cutting-edge AI-driven products that help businesses scale smarter and faster. We thrive on solving complex problems with simple, elegant solutions and are looking for leaders who can drive growth and partnerships as we scale.

Role Overview

Job Description

1. Engineering Skills

• Programming
• One of: TypeScript / Java / Go/Python
• DevOps / MLOps
• CI/CD
• Infrastructure as Code (Terraform / CDK)
• Monitoring (logs, traces, metrics)

2. Architecture Skills

• Identity & access
• AuthN/AuthZ (OAuth2, OIDC, JWT), SSO
• Role-based access control (RBAC)
• System design & scalability
• Designing modular, event-driven, or microservice architectures
• Handling growth, latency, and cost trade-offs
• Cloud architecture
• AWS / Azure / GCP (IAM, networking, storage, compute)
• Serverless vs containerized workloads
• Multitenancy architecture
• APIs & integrations
• REST / GraphQL
• Third-party integrations (LLMs, data providers, internal services)

3. Data Architecture

• Data pipelines
• Ingestion, cleaning, enrichment
• Streaming vs batch
• Storage
• Relational DBs + NoSQL
• Data lakes/warehouses
• Vector data management
• Hybrid search (keyword + vector)
• Data governance
• Access control
• Auditability (especially for enterprise customers)

Nice to have

• AI & ML Integration (Not Pure Data Science)
• Youre usually not expected to build models from scratch, but you must know:
• LLM integration
• OpenAI / Anthropic / open-source models
• Prompt engineering patterns
• Function calling / tool use
• RAG (Retrieval Augmented Generation)
• Vector databases (Pinecone, Weaviate, FAISS)
• Embeddings, chunking strategies
• Inference architecture
• Latency vs accuracy trade-offs
• Caching strategies (prompt & response caching)
• Model lifecycle awareness
• Versioning
• Monitoring hallucinations, drift, and cost usage

2. Product & Business Thinking (Often Overlooked)

• Startup architects must think beyond tech:
• MVP-first mindset
• Build good enough, iterate fast
• Buy vs build decisions
• When to use APIs vs custom solutions
• User experience with AI
• Guardrails
• Explainability
• Failure handling
• ROI awareness
• AI features tied to business value, not hype

3. Soft Skills (Extremely Important in Startups)

• More important than in large enterprises:
• Translating business problems AI solutions
• Communicating trade-offs to founders & investors
• Mentoring engineers
• Working with ambiguity and incomplete requirements

Experience Required - 8-10 Years