Lead Solution Architect

Lead Solution Architect

Job Overview:

We are looking for a Solution Architect to bridge the gap between advanced Data Science and

production-grade applications. You will be responsible for designing the end-to-end

architecture for Agentic AI systems, Chatbots, and Voice-enabled solutions, ensuring they

are seamlessly integrated into our application ecosystem. The ideal candidate understands that

an AI solution is only as good as its integration, security, and scalability. You will define the

infrastructure prerequisites, oversee API-driven microservices, and lead the alignment

between Data Engineering and Application development teams.

Key Responsibilities

• AI Integration Architecture: Design and implement architectures for integrating LLMs

(Large Language Models), Agentic AI workflows, and specialized Data Science models

into web and mobile applications.

• Voice & NLP Solutions: Architect solutions involving Speech-to-Text (STT), Text-to-

Speech (TTS), and Natural Language Understanding (NLU) for interactive user

experiences.

• Infrastructure Design: Define the infrastructure prerequisites (GPU/CPU requirements,

Vector Databases like Pinecone/Weaviate, and Scalable Storage) needed to support AI

workloads.

• Microservices & APIs: Build and oversee API-first architectures to ensure modularity

and high availability using REST, GraphQL.

• AI/ML Integration: Hands-on experience with LangChain, AutoGPT, or CrewAI for

building autonomous agents.

• Data Security & Privacy: Implement robust security frameworks for AI, including PII

masking, data encryption at rest/transit, and ensuring compliance with global data

residency laws.

• Cross-Functional Alignment: Act as the "glue" between Data Engineering (data

pipelines/feature stores) and Application teams (frontend/backend) to ensure a

cohesive end-to-end flow.

• Technical Pre-requisites: Conduct feasibility studies and define technical requirements

(latency thresholds, throughput, and token management) before project kickoff.

Technical Stack:

• Expertise in one of the programming languages like .Net or python with React and Node

JS, JavaScript

• Hands-on experience with LangChain, AutoGPT, or CrewAI for building autonomous

agents.

• Cloud Platforms: Deep knowledge of Azure AI Services (OpenAI, Speech Service)

• Hands-on experience in Azure for application/model deployment like azure app

service, or AKS.

• Strong understanding of CICD using Github action.