Technical Architect — Agentic AI

Winwire Technologies; Hyderabad; On-site; INR 2,000,000 - 3,000,000; Full time

Job summary

Winwire Technologies is seeking a seasoned Technical Architect specializing in generative AI solutions. Based in Hyderabad, the role involves designing secure AI architectures and implementing cloud-native solutions using Azure and modern AI frameworks.

The ideal candidate will have extensive experience in Python and Azure AI services, and a proven ability to mentor and collaborate with cross-functional teams. This position offers opportunities for innovation and leadership in a dynamic environment.

Qualifications

• 2–3 years of experience in designing and implementing generative AI solutions.
• 7+ years of experience in Python.
• 3–5 years of experience with Azure cloud services relevant to AI.

Responsibilities

• Lead the design of architecture for generative AI solutions within the Azure ecosystem.
• Design and develop AI architectures, frameworks, and algorithms that support large-scale AI solutions.
• Optimize AI models and systems for performance, scalability, and efficiency.

Skills

Generative AI Expertise

Python

Azure Proficiency

Architecture Design

Prompt Engineering

Multi-Agent Orchestration

Tools

Azure Machine Learning

Docker

Kubernetes

CI/CD

Terraform

Job description

Technical Architect — Agentic AI

Experience: 14–17 years

Location: Hyderabad / Bengaluru

Role Summary

We are looking for a seasoned GenAI Technical Architect with expertise in architecting and implementing enterprise‑scale generative AI and agentic AI solutions using Python, Azure AI services, and modern AI frameworks. The role focuses on defining scalable and secure AI architectures across LLMs, prompt engineering, fine‑tuning, GenAI SDKs, and multi‑agent orchestration, while driving cloud‑native solution design using distributed systems, microservices, Docker, Kubernetes, and Azure platforms such as AML, Cognitive Services, Databricks, and AKS.

What You’ll Do

• Lead the design of architecture for generative AI solutions within the Azure ecosystem. Collaborate with project managers, data scientists, software engineers, and other stakeholders to understand business goals and determine AI requirements.
• Design and develop AI architectures, frameworks, and algorithms that can support large‑scale and complex AI solutions.
• Evaluate and select appropriate AI technologies, tools, and frameworks to achieve desired performance, accuracy, and scalability.
• Lead the development and implementation of AI models, ensuring adherence to best practices in machine learning and deep learning.
• Develop and maintain AI pipelines, incorporating data cleaning, pre‑processing, feature engineering, model training, and validation processes.
• Optimize AI models and systems for performance, scalability, and efficiency.
• Conduct regular code reviews and provide technical guidance to junior members of the team.
• Stay up to date with the latest advancements in AI technologies, frameworks, and algorithms, and identify opportunities for their application in the organization.
• Collaborate with infrastructure teams to ensure smooth deployment and monitoring of AI models in production environments.
• Document AI architectures, design decisions, and technical specifications for reference and knowledge sharing.
• Leadership and Collaboration: Exceptional leadership skills with the ability to lead cross‑functional teams and drive architectural decisions. Proven experience in mentoring junior team members, conducting code reviews, and fostering a culture of innovation and collaboration.
• Problem‑solving and Innovation: Strong analytical and problem‑solving skills, with a track record of proposing innovative solutions to complex technical challenges. Ability to stay updated on emerging AI technologies, best practices, and industry trends to drive continuous improvement.

Must‑Have Skills

• Generative AI Expertise: 2–3 years of experience in designing and implementing generative AI solutions, including knowledge of various generative and autoregressive models. Ability to apply generative AI techniques to diverse use cases such as image generation, text generation, and creative content synthesis.
• 2 years of experience in prompt engineering, fine tuning, agentic framework, GenAI SDK’s
• 1–2 years of experience in Agentic AI frameworks like AutoGen, Lann graph, MS Agent SDK, A2A, MCP, A2P, memory concepts, multi‑agent orchestration
• 7+ years of experience in Python
• Azure Proficiency: 3–5 years of experience with Azure cloud services relevant to AI, including Azure Machine Learning, Azure Cognitive Services, Azure Databricks, and Azure Kubernetes Service (AKS). 2+ years of experience in Azure’s capabilities to architect end‑to‑end AI solutions and optimize performance.
• Architecture Design: 5+ years of skills with the ability to design scalable, reliable, and cost‑effective architectures for AI solutions. Proficiency in designing distributed systems, microservices architectures, and containerized solutions using technologies such as Docker and Kubernetes.

Good to Have

• Security and Compliance: Understanding of security principles and best practices in AI development, with the ability to implement security controls, encryption mechanisms, and access management policies to protect AI models and sensitive data.
• Integration and Deployment: Proficiency in implementing CI/CD pipelines, automation scripts, and infrastructure as code (IaC) using tools such as Azure DevOps, Terraform, or Ansible. Experience in containerization and orchestration of AI workloads using Docker and Kubernetes.
• Software Development: Strong programming skills in languages such as Python, with experience in developing AI applications, RESTful APIs, and microservices architectures. Familiarity with software development methodologies such as Agile or Scrum.
• Communication and Presentation: Excellent communication skills with the ability to convey complex technical concepts to non‑technical stakeholders. Experience in preparing and delivering technical presentations, architecture diagrams, and documentation to communicate architectural decisions and design rationale effectively.

Tech Stack

Snowflake, AWS S3, Matillion, dbt (data build tool), Collibra, Profisee MDM, Python, SQL, Snowflake Cortex AI, WinAIDM, SnowConvert AI, Tableau, Power BI, CI/CD (Azure DevOps), Git, ACORD Data Model