Technical Architect - Data Science & Gen AI

WinWire; Bengaluru, Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

WinWire is seeking a Technical Architect specializing in LLMs and Agentic AI to own the architecture, strategy, and delivery of enterprise‑grade GenAI solutions. You will collaborate with customers and cross‑functional teams to define the AI roadmap and scalable deployments across Azure and GCP.

The role demands strong backend, data, and governance expertise, with hands-on work on RAG pipelines, multi‑agent orchestration, LLMOps, and secure, compliant implementations.

Qualifications

• GenAI solution architecture expertise with RAG, LangChain, and prompt design.
• 5+ years in backend microservices, APIs, and cloud orchestration across Azure/GCP.
• 2-3 years enterprise LLM architecture with Azure OpenAI, Vertex AI, or HF models.
• 2-3 years designing RAG pipelines and vector search with Pinecone/Weaviate/FAISS.
• 2-3 years LLM optimization and PEFT approaches (LoRA/QLoRA).
• 2-3 years multi-agent orchestration with LangChain, AutoGen, or DSPy.
• 2-3 years performance engineering for latency and scalability on cloud.
• 2-3 years integrating OpenAI & third-party models via APIs/connectors.
• 1-2 years governance, guardrails, data protection, and responsible AI practices.

Responsibilities

• Architect scalable GenAI solutions across Azure and GCP.
• Provide technology strategy and guidance to customers and teams.
• Lead LLM-driven apps, RAG pipelines, and agentic AI workflows.
• Define agentic AI architectures using LangGraph, AutoGen, DSPy.
• Oversee integration of models via MuleSoft Apigee connectors.
• Establish LLMOps with CI/CD, monitoring, and cost control.
• Lead governance reviews and design authority for LLM initiatives.
• Collaborate with data scientists and engineers to translate use cases.
• Document standards and playbooks for enterprise adoption.
• Implement Monitoring & Observability dashboards.

Skills

Generative AI Solution Architecture

Backend Architecture

Enterprise LLM Architecture

RAG & Data Pipeline Design

LLM Optimization & Adaptation

Multi-Agent Orchestration

Performance Engineering

AI Application Integration

Governance & Guardrails

Tools

LangChain

LangGraph

Azure OpenAI

Azure Functions

GCP Vertex AI

Hugging Face models

MuleSoft

Apigee

Kubernetes

Job description

Job description

Primary Skills (Must Have experience):

• Generative AI Solution Architecture (2-3 years): Proven experience in designing and architecting GenAI applications, including Retrieval-Augmented Generation (RAG), LLM orchestration (LangChain, LangGraph), and advanced prompt design strategies.
• Backend & Integration Expertise (5+ years): Strong background in architecting Python-based microservices, APIs, and orchestration layers that enable tool invocation, context management, and task decomposition across cloud-native environments (Azure Functions, GCP Cloud Functions, Kubernetes).
• Enterprise LLM Architecture (2-3 years): Hands-on experience in architecting end-to-end LLM solutions using Azure OpenAI, Azure AI Studio, Hugging Face models, and GCP Vertex AI, ensuring scalability, security, and performance.
• RAG & Data Pipeline Design (2-3 years): Expertise in designing and optimizing RAG pipelines, including enterprise data ingestion, embedding generation, and vector search using Azure Cognitive Search, Pinecone, Weaviate, FAISS, or GCP Vertex AI Matching Engine.
• LLM Optimization & Adaptation (2-3 years): Experience in implementing fine-tuning and parameter-efficient tuning approaches (LoRA, QLoRA, PEFT) and integrating memory modules (long-term, short-term, episodic) to enhance agent intelligence.
• Multi-Agent Orchestration (2-3 years): Skilled in designing multi-agent frameworks and orchestration pipelines with LangChain, AutoGen, or DSPy, enabling goal-driven planning, task decomposition, and tool/API invocation.
• Performance Engineering (2 - 3 years): Experience in optimizing GCP Vertex AI models for latency, throughput, and scalability in enterprise-grade deployments.
• AI Application Integration (2 - 3 years): Proven ability to integrate OpenAI and third-party models into enterprise applications via APIs and custom connectors (MuleSoft, Apigee, Azure APIM).
• Governance & Guardrails (1 - 2 years): Hands-on experience in implementing security, compliance, and governance frameworks for LLM-based applications, including content moderation, data protection, and responsible AI guardrails.

Role & responsibilities

As a Technical Architect specializing in LLMs and Agentic AI, you will own the architecture, strategy, and delivery of enterprise-grade AI solutions. You will work with cross-functional teams and customers to define the AI roadmap, design scalable solutions, and ensure responsible deployment of Generative AI across the organization:

Responsibilities:

• Architect Scalable GenAI Solutions: Lead the design of enterprise architectures for LLM and multi-agent systems, ensuring scalability, resilience, and security across Azure and GCP platforms.
• Technology Strategy & Guidance: Provide strategic technical leadership to customers and internal teams, aligning GenAI projects with business outcomes.
• LLM & RAG Applications: Architect and guide development of LLM-powered applications, assistants, and RAG pipelines for structured and unstructured data.
• Agentic AI Frameworks: Define and implement agentic AI architectures leveraging frameworks like LangGraph, AutoGen, DSPy, and cloud-native orchestration tools.
• Integration & APIs: Oversee integration of OpenAI, Azure OpenAI, and GCP Vertex AI models into enterprise systems, including MuleSoft Apigee connectors.
• LLMOps & Governance: Establish LLMOps practices (CI/CD, monitoring, optimization, cost control) and enforce responsible AI guardrails (bias detection, prompt injection protection, hallucination reduction).
• Enterprise Governance: Lead architecture reviews, governance boards, and technical design authority for all LLM initiatives.
• Collaboration: Partner with data scientists, engineers, and business teams to translate use cases into scalable, secure solutions.
• Documentation & Standards: Define and maintain best practices, playbooks, and technical documentation for enterprise adoption.
• Monitoring & Observability: Guide implementation of AgentOps dashboards for usage, adoption, ingestion health, and platform performance visibility.