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.
Development Tools and Methodologies
Essential
Docker for containerizing AI applications and services.
Kubernetes for deploying and managing AI workloads.
API Management for governing secure and scalable API consumption.
Microservices architecture and enterprise integration patterns.
MLOps and AI lifecycle management.
Model deployment, monitoring, observability and production AI operations.
Architecture reviews, technical governance, code reviews and solution-quality assessments.
Experience managing complex technical delivery programs from ideation to production.
Preferred
Experience with infrastructure-as-code and automated environment provisioning.
Experience wit