Key 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 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->Agile Testing->Agile Testing - ALL->CD/CI,Technology->AI-Agentic AI->Agent Engineering,Technology->AI-AI Engineering->LLMOps,Technology->AI-AI Engineering->Model Deployment (Kubernetes),Technology->AI-Generative AI->Generative AI - Basic->model framework (langchain),Technology->Architecture->Architecture - ALL,Technology->AI-Generative AI->Artificial Intelligence - BASIC,Technology->Open System->Open System- ALL->Python

📌 AI Engineering Architect (India)
🏢 Infosys
📍 India