Ai Engineering Architect - PAN INDIA

Infosys; Pune District, Chennai District, Bengaluru; Hybrid; INR 1,500,000 - 2,000,000; Full time

Job summary

Infosys in Pune seeks a senior AI Architect to define reference architectures for generative AI and agentic systems, and to lead platform design across AI pipelines and workflows.

You will work with LLMs, orchestration frameworks, and enterprise integrations, ensuring scalable, observable, and cost-efficient solutions while mentoring engineers on AI engineering best practices.

Qualifications

• 13+ years of software engineering experience with 3+ years in AI and architecture ownership.
• Proven track record designing enterprise-scale AI engineering or MLOps platforms.
• Strong hands-on experience with LLMs, prompt engineering, RAG, and agent frameworks.
• Proficiency in Python, AI frameworks, and cloud-native AI services.
• Experience with Kubernetes, CI/CD, and secure deployment of AI models.
• Capability to integrate AI across enterprise-scale systems.

Responsibilities

• Define and own AI reference architectures for generative AI, agentic systems, and AI augmented applications.
• Architect scalable solutions using LLMs, multi-agent systems, orchestration frameworks, and AI pipelines.
• Design AI platforms supporting model serving, prompt management, RAG, and workflow orchestration.
• Establish architectural standards for performance, scalability, reliability, and cost efficiency.
• Build reusable AI components for LLM integration, vector search, embeddings, and inference services.
• Enable secure and scalable deployment using Kubernetes, serverless platforms, and CI/CD pipelines.
• Integrate AI capabilities into enterprise systems using APIs, SDKs, and event-driven architectures.
• Collaborate with QE teams to embed AI into test automation, test data generation, and intelligent validation.
• Define guardrails for model lifecycle, versioning, monitoring, and rollback.
• Ensure adherence to non-functional requirements including performance, observability, and fault tolerance.
• Leverage observability tools to monitor model performance and drift.
• Review designs and implementations for architectural compliance and code quality.
• Mentor engineers and architects on AI engineering best practices.

Skills

AI architecture

MLOps platforms

LLMs / prompt engineering

Python

Kubernetes

CI/CD

Cloud-native AI services

Agent frameworks

Observability tooling

Tools

LangChain

OpenSearch

Pinecone

FAISS

Weaviate

OpenTelemetry

Prometheus

Grafana

GitHub Actions

Azure DevOps

Jenkins

Job description

Role & responsibilities:

AI Architecture & Engineering

• Define and own AI reference architectures for generative AI, agentic systems, and AI augmented applications
• Architect scalable solutions using LLMs, multi agent systems, orchestration frameworks, and AI pipelines
• Design AI platforms supporting model serving, prompt management, RAG, and workflow orchestration
• Establish architectural standards for performance, scalability, reliability, and cost efficiency

Platform Engineering & Integration

• Build reusable AI components for LLM integration, vector search, embeddings, and inference services
• Enable secure and scalable deployment using Kubernetes, serverless platforms, and CI/CD pipelines
• Integrate AI capabilities into enterprise systems using APIs, SDKs, and event driven architectures
• Collaborate with QE teams to embed AI into test automation, test data generation, and intelligent validation

Engineering Governance & Quality

• Define architectural guardrails for model lifecycle, versioning, monitoring, and rollback
• Ensure adherence to non functional requirements including performance, observability, and fault tolerance
• Leverage observability tools to monitor model performance and drift
• Review designs and implementations for architectural compliance and code quality
• Mentor engineers and architects on AI engineering best practices

Core Platforms, Frameworks & Tooling

• LLM and foundation model platforms (e.g., AWS Bedrock, Azure OpenAI, Vertex AI)
• Agentic AI and orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen, Google ADK or equivalent)
• Vector databases and search technologies (OpenSearch, Pinecone, FAISS, Weaviate)
• Model lifecycle and deployment tooling (Kubernetes, containers, serverless runtimes)
• CI/CD and MLOps tooling for AI pipelines (GitHub Actions, Azure DevOps, Jenkins)
• Observability and monitoring tooling for AI systems (OpenTelemetry, Prometheus, Grafana)

Client Orientation & Leadership

• Partner with product and engineering teams to identify AI opportunities and shape roadmaps
• Support client workshops, RFPs, and solution presentations
• Mentor engineers on AI/ML/Gen AI best practices and emerging technologies
• Translate complex AI concepts into business-friendly narratives.

Preferred candidate profile

• 13+ years of experience in software engineering with 3+ years in AI with strong architecture ownership
• Proven experience designing and implementing enterprise-scale AI engineering or MLOps platforms
• Strong hands on experience with LLMs, prompt engineering, RAG, and agent frameworks
• Proficiency in Python, AI frameworks, and cloud-native AI services
• Experience in Kubernetes, CI/CD, and secure deployment of AI models
• Experience integrating AI capabilities into enterprise scale systems

Good to Have Skills

• Experience with multi agent orchestration and autonomous workflows
• Knowledge of model observability and monitoring tooling
• Exposure to QE platforms, test automation frameworks, or AI assisted testing
• Domain experience in regulated industries such as BFSI, Healthcare, Telecom
• Cloud and AI certifications