AI Engineering Architect

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 • 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 Preferred Skills: Technology->Agile Testing->Agile Testing - ALL,Technology->AI-AI Engineering->AI/ML Solution Architecture and Design,Technology->AI-AI Engineering->Databricks AI Engineering Services,Technology->AI-AI Engineering->LLMOps,Technology->AI-AI Engineering->MLOps,Technology->AI-AI Engineering->Model Optimization,Technology->AI-AI Engineering->Model Support,Technology->AI-Generative AI->Conversational AI Platform,Technology->AI-Generative AI->Generative AI - Basic->chains,Technology->AI-Generative AI->Generative AI for Data Analytics,Technology->AI-Generative AI->Prompt Engineering,Technology->Architecture->Architecture - ALL,Technology->Enterprise Architecture->Digital Architecture Skills: Jenkins, Kubernetes, Grafana, Apis, Prometheus, Azure Devops Experience: 13.00-15.00 Years