AI Engineering Architect

Job description

- 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 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.