AI Engineering Architect

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 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 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