Educational Requirements

Bachelor of Engineering

Service Line

Infosys Quality Engineering

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

Technical and Professional Requirements:
- 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

Valuable 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

Preferred Skills:
- Technology->Architecture->Architecture - ALL
- Technology->Agile Testing->Agile Testing - ALL
- Technology->Enterprise Architecture->Digital Architecture
- Technology->AI-Generative AI->Conversational AI Platform
- Technology->AI-Generative AI->Generative AI - Basic->chains
- Technology->AI-AI Engineering->AI/ML Solution Architecture and Design
- Technology->AI-AI Engineering->MLOps
- Technology->AI-AI Engineering->Model Optimization
- Technology->AI-AI Engineering->Model Support
- Technology->AI-Generative AI->Generative AI for Data Analytics
- Technology->AI-Generative AI->Prompt Engineering
- Technology->AI-AI Engineering->Databricks AI Engineering Services
- Technology->AI-AI Engineering->LLMOps

📌 AI Engineering Architect (Bengaluru)
🏢 Infosys
📍 Bengaluru