AI Engineering Architect

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

Education

Bachelor of Engineering