AI Engineering Architect

Key Responsibilities:

• Engineering AI Architectures
• Establish and manage reference architectures for generative AI systems and applications enhanced by AI
• Design scalable solutions utilizing LLMs, multi-agent systems orchestration frameworks, and AI pipelines
• Create AI platforms that facilitate model serving, prompt management, RAG, and workflow orchestration
• Set architectural benchmarks for performance, scalability, reliability, and cost-effectiveness
• Platform Engineering and Integration
• Develop reusable AI components for LLM integration, vector search, embeddings, and inference services
• Facilitate secure and scalable deployments through Kubernetes, serverless platforms, and CI/CD pipelines
• Incorporate AI features into enterprise systems via APIs, SDKs, and event-driven architectures
• Work alongside QE teams to integrate AI into test automation, test data generation, and intelligent validation
• Governance and Quality in Engineering
• Establish architectural guidelines for model lifecycle management, versioning, monitoring, and rollback processes
• Ensure compliance with non-functional requirements such as performance, observability, and fault tolerance
• Utilize observability tools to track model performance and drift
• Evaluate designs and implementations for architectural integrity and code quality
• Guide engineers and architects in AI engineering best practices
• Core Platforms, Frameworks, and Tools
• Platforms for LLMs and foundational models, such as e
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• AWS Bedrock, Azure OpenAI, Vertex AI
• Agentic AI and orchestration frameworks like LangChain, LangGraph, CrewAI, AutoGen, Google ADK, or their equivalents
• Technologies for vector databases and search, including OpenSearch, Pinecone, FAISS, Weaviate
• Tools for model lifecycle management and deployment, such as Kubernetes, containers, and serverless runtimes
• CI/CD and MLOps tools for AI pipelines like GitHub Actions, Azure DevOps, Jenkins
• Monitoring and observability tools for AI systems, including OpenTelemetry, Prometheus, Grafana
• Client Engagement and Leadership
• Collaborate with product and engineering teams to pinpoint AI opportunities and shape development roadmaps
• Assist in client workshops, RFPs, and solution presentations
• Mentor engineers on AI, ML, and generative AI best practices and emerging technologies
• Convert complex AI concepts into accessible business narratives

Technical Requirements:

• 13+ years of software engineering experience, including 3+ years in AI with a focus on architecture ownership
• Demonstrated success in designing and implementing enterprise-grade AI engineering or MLOps platforms
• Extensive hands-on experience with LLMs, prompt engineering, RAG, and agent frameworks
• Proficient in Python, AI frameworks, and cloud-native AI services
• Experienced with Kubernetes, CI/CD, and the secure deployment of AI models
• Proven history of integrating AI capabilities into large-scale enterprise systems
• Desirable Skills
• Familiarity with multi-agent orchestration and autonomous workflows
• Knowledge of model observability and monitoring tools
• Experience with QE platforms, test automation frameworks, or AI-assisted testing
• Background in regulated industries such as BFSI, Healthcare, or Telecom
• Hold relevant cloud and AI certifications

Preferred Skills:

Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->generative ai,Technology->AI-Generative AI->Conversational AI Platform->Kore.AI,Technology->AI-Generative AI->Generative AI - Basic,Technology->AI-AI Engineering->MLOps,Technology->AI-Generative AI->Artificial Intelligence - BASIC,Technology->Architecture->Architecture - ITIL