AI Architect

Company: Sigma Solve

Location: Ahmedabad / Pune, India

Job Type: Full-Time

Experience: 5+ years in software engineering; strong hands-on LLM/GenAI development experience required

About Sigma Solve

Sigma Solve is a software services firm with over 15 years of delivery experience, building complex technology platforms for clients across Logistics, Healthcare, Commerce, and other enterprise domains.

We deliver custom software platforms, mobile applications, data solutions, automation, and digital transformation initiatives for businesses that need software built well and delivered on time.

AI is becoming central to both how we deliver software and how our clients operate. We are now building our AI practice from the ground up, and we are looking for an AI Architect to play a key role in defining and building that foundation.

About the Role

As an AI Architect, you will design and build the technical foundation for Sigma Solve's AI capability.

This is a hands-on architecture and engineering role, particularly during the first year. You will work across client engagements and internal initiatives, designing AI architectures, building core AI systems, establishing engineering standards, and guiding a small team of AI engineers.

You will work closely with the Product Head and Solution Architects to identify and scope AI use cases for new and existing clients.

As the AI practice grows, the role will progressively evolve toward architecture, technical leadership, and governance. However, in Year 1, you will be expected to build.

Key Responsibilities

- Design AI architectures for new client engagements, including:
- Model selection
- Data flows and processing
- Integration patterns
- Cost management
- Security and governance controls
- Design, build, and govern an AI Gateway that centralizes AI model calls, logging, monitoring, and cost control.
- Design and deliver RAG (Retrieval-Augmented Generation) platforms for internal and client-facing use cases.
- Build reusable AI components such as:
- Document extraction
- Knowledge retrieval
- AI-assisted QA
- LLM-powered automation
- Define and enforce AI development standards across the organization.
- Establish best practices around AI tool usage, logging, security, output validation, and governance.




- Lead and mentor a small team of AI Engineers.
- Assign AI engineers across client engagements while maintaining a strong technical foundation and reusable architecture.
- Work with Product and Solution Architecture teams to identify and scope practical AI use cases.
- Identify opportunities to introduce AI into existing software development and delivery projects.
- Participate directly in client engagements when architecture or hands-on engineering expertise is required.

Technology Environment We use a modern, practical, and model-agnostic AI stack, selecting the right technology based on the business use case.

AI / LLM:

Claude API, Gemini API, Open Models, Azure AI Services

RAG / Retrieval:

Snowflake Vector Search, Vector Embeddings, Retrieval Pipelines

Document AI:

Azure Document Intelligence, Multimodal AI / Document Extraction

Data & AI Platform:

Snowflake, Snowflake Cortex AI

Backend:

Node.js, .NET, REST APIs

Frontend / Mobile:

React, Next.js, Flutter

Cloud:

AWS, Azure

Engineering / DevOps:

GitLab, GitLab CI/CD, SonarQube

What We Are Looking For

We are not looking for someone simply because they have spent 10+ years in AI.

We are looking for a robust software engineer who has actually built LLM-powered systems and understands what it takes to move AI from experimentation into real applications.

The scale of the system matters less than the depth of your hands-on experience. A startup project, freelance implementation, POC, or enterprise platform can all be relevant.

Essential Requirements

1. Hands-on LLM Development – Mandatory

You must have built a system where an LLM is a core component.

You should have experience with:

- Calling LLM APIs programmatically
- Chaining multiple AI/LLM steps
- Handling model failures and errors
- Managing prompts and model outputs
- Deploying an AI/LLM solution for actual use

You should be able to clearly explain what you built,



what broke, and how you fixed it. 2. RAG Experience – Mandatory

Hands-on experience designing or building a RAG pipeline is required.

You should understand

- Document chunking
- Embedding generation
- Vector search
- Context retrieval
- Prompt/context injection
- Retrieval quality and relevance
- Diagnosing retrieval degradation

Both POC and production experience will be considered. 3. Claude or Gemini API – Mandatory

You must have used Claude API or Gemini API programmatically, rather than only using the tools through a chat interface.

You should understand

- Prompt structure
- Token limits
- API integration
- Cost implications
- Model selection
- Error handling
- Production considerations

4. Strong Software Engineering Fundamentals – Mandatory You should have solid experience with:

- API design
- Service architecture
- Backend development
- Error handling
- Version control
- Production software development

You should be technically credible to experienced software engineers. 5. GitLab Experience – Mandatory

Hands-on experience with GitLab is required, including:

- Merge Requests (MRs)
- CI/CD pipelines
- Branching strategies
- Branch governance
- Code collaboration and review

6. Hands-on Mindset – Mandatory This is not a purely governance or advisory role.

You should be comfortable designing, coding, debugging, deploying, and improving AI systems yourself, particularly during the first year.

Good to Have The following experience will be an advantage but is not mandatory:

- Snowflake, Snowflake Vector Search, or Snowflake Cortex AI
- Data pipeline architecture
- Azure Document Intelligence
- Structured document extraction
- Reusable AI architecture or reference architecture design
- Agentic AI / AI agent workflows
- Multi-step AI orchestration
- Human-in-the-loop AI systems
- State management across AI workflows
- Strong Node.js or .NET experience
- Flutter / Dart experience
- Experience working in a software services or consulting organization
- Experience with healthcare technology or compliance-oriented systems
- HIPAA-adjacent architecture
- PHI masking
- AI audit trails and governance

Pay: ₹1,500,000.00 - ₹2,500,000.00 per year

Work Location: In person

📌 AI Architect (Pune)
🏢 Sigma Solve
📍 Pune