Roles ResponsibilitiesAI 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 Requirement
- 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

- Robust 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
Educational RequirementBachelor of Engineering
Preferred SkillsTechnology->AI-AI Engineering->AI/ML Solution Architecture and Design->generative ai,Technology->AI-Generative AI->Conversational AI Platform->Kore.AI,Technology->AI-Generative AI->Generative AI - Basic,Technology->AI-AI Engineering->MLOps,Technology->AI-Generative AI->Artificial Intelligence - BASIC,Technology->Architecture->Architecture - ITIL
Service LineInfosys Quality Engineering

📌 AI Engineering Architect (Bengaluru)
🏢 Infosys
📍 Bengaluru