AI Architecture – Enterprise GenAI, LLMs, RAG, Agentic AI, Azure/AWS & MLOps

Job Summary

Synechron is seeking an AI Architecture with 10+ years of experience to lead the design, implementation and governance of enterprise-scale Artificial Intelligence and Generative AI solutions. The role combines AI architecture, cloud-native platform design, technical leadership, stakeholder management and AI strategy execution.

The successful candidate will guide multidisciplinary teams, establish scalable and secure AI platforms, support enterprise-wide Generative AI adoption and ensure that AI initiatives align with business objectives.

Software Requirements

Required

• Azure and/or AWS: Extensive experience designing and delivering cloud-based AI platforms using current project-supported services.
• Azure OpenAI Services: Experience architecting and integrating LLM-based applications.
• AWS Bedrock: Experience using managed foundation-model services for enterprise AI solutions.
• LangChain and LangGraph: Experience designing LLM orchestration, RAG pipelines and agentic workflows.
• Model Context Protocol (MCP): Working knowledge of MCP concepts and their use in AI application integration.
• Vector Databases: Experience with vector storage, embeddings, indexing and semantic retrieval.
• Knowledge Graphs and Semantic Search: Experience designing or integrating knowledge-based search solutions.
• Docker and Kubernetes: Experience containerizing, deploying and managing AI workloads.
• API Management: Experience designing and governing secure API integrations.
• Microservices Architecture: Experience designing scalable, modular and integrated enterprise applications.
• MLOps Tools and Platforms: Experience supporting model deployment, monitoring, observability and AI lifecycle management.
• AI Governance Tools and Frameworks: Experience implementing responsible AI, guardrails, risk management and governance controls.
• Architecture and Collaboration Tools: Experience using tools for architecture documentation, technical reviews, delivery governance and stakeholder communication.

Preferred

• Exposure to the Microsoft Copilot ecosystem and enterprise AI solutions.
• Experience with AI security and compliance frameworks.
• Experience with enterprise architecture frameworks.
• Knowledge of Blockchain, Cloud Transformation and Digital Platforms.
• Experience with tools supporting model evaluation, AI observability and production operations.
• Experience with reusable AI platform frameworks and enterprise technology standards.

Overall Responsibilities

AI Strategy and Leadership

• Define and execute Synechron’s AI and Generative AI roadmap in alignment with business objectives.
• Lead AI transformation initiatives and support enterprise-wide AI adoption.
• Partner with business leaders, product owners and executive stakeholders to identify AI opportunities and develop innovation strategies.
• Build, mentor and support multidisciplinary AI engineering, architecture and data science teams.
• Establish AI governance, responsible AI practices, risk management frameworks and decision-making processes.
• Define measurable outcomes for AI initiatives, including adoption, solution quality, operational performance, risk reduction and business value.

Architecture and Solution Design

• Architect enterprise-scale AI and Generative AI platforms and solutions.
• Define end-to-end architectures covering:Data ingestionModel orchestrationRAG pipelinesVector databasesAI agentsEnterprise integrationsSecurityMonitoring and observability
• Drive architecture decisions for LLM-based applications using Azure OpenAI, AWS Bedrock and other foundation-model ecosystems.
• Establish AI platform standards, reusable frameworks, design patterns and enterprise best practices.
• Design scalable multi-agent and Agentic AI systems.
• Ensure architecture decisions address scalability, performance, reliability, security, compliance, maintainability and cost efficiency.

Delivery and Program Management

• Oversee the delivery of complex AI programs from ideation through production deployment.
• Manage architecture reviews, technical governance, solution quality and delivery risks.
• Collaborate with engineering, DevOps, MLOps and cloud teams to enable successful deployments.
• Ensure AI solutions meet agreed technical, functional, security, compliance and operational requirements.
• Track program dependencies, milestones, risks, decisions and outcomes.
• Drive continuous improvement and innovation across AI initiatives.
• Support sustainable AI delivery by promoting reusable components, efficient model usage, optimized infrastructure and maintainable platform designs.

Stakeholder and Team Management

• Communicate AI strategy, architecture options, technical risks and delivery progress to technical and non-technical stakeholders.
• Influence architecture decisions at leadership levels through evidence-based recommendations.
• Mentor architects, AI engineers and technical leads.
• Facilitate technical discussions, design reviews, governance forums and solution-approval sessions.
• Build effective collaboration across business, product, engineering, data, security, DevOps and MLOps teams.

Technical Skills (By Category)

Programming Languages

Essential

• Strong software engineering experience relevant to AI, Generative AI, cloud platforms and enterprise application integration.
• Ability to assess implementation approaches, review technical designs and guide engineering teams in developing production-grade AI solutions.
• Ability to understand and govern code quality, integration patterns, deployment requirements and maintainability standards.

Preferred

• Hands-on experience with Python for AI/ML solution development.
• Experience with additional programming languages used in APIs, microservices or enterprise platforms.

Databases and Data Management

Essential

• Experience with vector databases, embeddings, indexing and semantic search.
• Experience with knowledge graphs and knowledge-based retrieval.
• Strong understanding of data engineering and enterprise integration patterns.
• Ability to define data ingestion, processing, storage, retrieval, quality and governance requirements.
• Understanding of structured, unstructured and semi-structured data used by AI applications.

Preferred

• Experience designing large-scale data platforms, data lakes or distributed data-processing solutions.
• Experience integrating knowledge graphs with RAG and Agentic AI solutions.
• Experience with data lineage, metadata management and enterprise data governance.

Cloud Technologies

Essential

• Extensive experience with Azure and/or AWS cloud platforms.
• Expertise in Azure OpenAI Services and AWS Bedrock.
• Experience designing secure and scalable cloud-native AI platforms.
• Understanding of cloud availability, scalability, resilience, monitoring, access management and cost optimization.
• Experience integrating managed AI services with enterprise applications and platforms.

Preferred

• Experience with cloud transformation programs.
• Experience designing multi-environment or multi-region AI platforms.
• Familiarity with infrastructure automation and cloud service optimization.

Frameworks and Libraries

Essential

• Strong expertise in Generative AI, LLMs, NLP and Transformer architectures.
• Advanced knowledge of prompt engineering and model evaluation.
• Experience with RAG architecture and implementation.
• Experience with LangChain and LangGraph.
• Experience with Agentic AI frameworks and multi-agent systems.
• Working knowledge of MCP.
• Experience implementing AI guardrails and responsible AI controls.
• Understanding of foundation-model ecosystems and LLM application patterns.

Preferred

• Exposure to Copilot solutions and enterprise AI assistants.
• Experience with multimodal AI solutions.
• Experience with LLM fine-tuning, model selection, response evaluation and quality measurement.
• Experience with reusable orchestration frameworks and AI platform components.

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