AI Technical Architect

About the Role

Technical Architect

multi-agent Generative AI frameworks

Azure AI Foundry, OpenAI, RAG pipelines, and enterprise datasets (Structured like Power BI and unstructured like Legal contracts)

Responsibilities

• Solution Architecture

• Design end-to-end architecture for

  multi-agent AI systems

  including orchestration, communication, and memory layers.
• Select and design frameworks (LangGraph, Semantic Kernel, etc.) for

  agent workflows and orchestration

  .
• Architect RAG pipelines to enable

  retrieval, reasoning, and response generation

  from structured, semi-structured, and unstructured sources.
• Design Scalable, modular, and secure system architecture using microservices and modern design patterns.
• Create high-level product specifications, architecture diagrams, and technical documentation.
• Provide architectural blueprints, guidance, and technical leadership to development teams.
• Architect and deploy scalable, secure solutions on Azure Cloud, using services like Azure Kubernetes Service (AKS), Azure Functions, Azure Storage, and Azure DevOps.
• Ensure robust CI/CD and DevSecOps practices in place to streamline deployments and enforce compliance.
• Guide the team in troubleshooting, code reviews, performance tuning, and ensuring adherence to software engineering best practices.
• Conduct regular technical reviews, present progress updates, and ensure timely delivery of milestones.

• AI/LLM Engineering

• Design and implement

  specialized AI agents

  (e.g., KPI query agent, forecasting agent, data integration agent).
• Optimize LLM usage with

  prompt engineering, fine-tuning, and tool integration

  .
• Ensure scalability, modularity, and fault tolerance in multi-agent workflows.

• Data Integration & Analytics

• Integrate AI agents with

  corporate datasets both structured and unstructured

• Work with financial KPIs across Key Figures, Financial Statements, Cash Flow, and Global Cost Benchmark reports.
• Enable

  predictive analytics, what-if scenarios, and simulations

  using ML models.

• Enterprise Readiness

• Ensure compliance with

  enterprise security, RBAC, and data governance policies

  .
• Define

  monitoring, observability, and cost optimization

  strategies for AI workloads.
• Collaborate with business SMEs and data engineers to align AI outputs with functional requirements.

Requirements

• 10+ years in technology with 4+ years in AI/ML and 2+ years in Generative AI/LLM solutions
• 3+ years of experience in a Software or Solution Architect role.
• Proven experience in designing and building production-grade solutions using microservices architecture.
• Proficiency in Python for AI/ML and agent development.
• Hands-on experience with Azure AI Foundry / Azure OpenAI Service (or AWS Bedrock, GCP Vertex AI).
• Strong knowledge of retrieval-augmented generation (RAG) design patterns.
• Experience integrating AI with structured and unstructured data sources
• Solid experience with Prompt Engineering, RAG pipelines, and Vector Database integration.
• Experience deploying AI/ML solutions on Azure Cloud, leveraging PaaS components and cloud-native patterns.
• Proficiency with containerization and orchestration tools (Docker, Kubernetes).
• Knowledge of architectural patterns: event-driven, domain-driven design, CQRS, service mesh, etc.
• Familiarity with NLP tools, frameworks, and libraries (spaCy, Hugging Face, Transformers, etc.).
• Strong communication and leadership skills to work cross-functionally with technical and non-technical teams.

Location

• Bangalore