Synechron Canada ULC_CANADA Company
From company careers site
AI Architecture – Enterprise GenAI, LLMs, RAG, Agentic AI, Azure/AWS & MLOps
- Location
- Bengaluru; Hyderabad; Pune; Mumbai
- Work mode
- On-site
- Employment type
- Full-time
About the job
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 with CI/CD and continuous delivery practices for AI platforms.
- Experience with model-performance monitoring, data-drift monitoring, latency tracking and operational dashboards.
- Familiarity with enterprise architecture frameworks and structured technology governance.
Security Protocols
Essential
- Experience designing secure AI platforms and enterprise integrations.
- Understanding of API security, identity management, authentication, authorization and access controls.
- Knowledge of AI governance, responsible AI, risk management and compliance practices.
- Ability to define controls for sensitive data, model access, prompt security, retrieved content and AI-generated outputs.
- Understanding of monitoring, auditability, traceability and human oversight for AI systems.
- Ability to ensure that AI solutions meet applicable security and compliance standards.
Preferred
- Experience with AI security frameworks and model-risk management.
- Experience designing controls for prompt injection, data leakage, unauthorized access and unsafe AI outputs.
- Experience delivering AI solutions in regulated or data-sensitive environments.
Experience Requirements
- At least 10 years of experience in AI architecture, software engineering, machine learning, data platforms, cloud architecture or related technical disciplines.
- Proven experience designing and delivering enterprise-scale AI and Generative AI solutions.
- Strong expertise in Generative AI, LLMs, NLP, Transformer architectures, prompt engineering and model evaluation.
- Experience with RAG, Agentic AI frameworks, LangChain, LangGraph, MCP, AI guardrails and multi-agent systems.
- Extensive experience with Azure and/or AWS, including Azure OpenAI Services and AWS Bedrock.
- Experience with Kubernetes, Docker, API Management and microservices architecture.
- Experience with vector databases, knowledge graphs, semantic search, data engineering and enterprise integration patterns.
- Strong understanding of MLOps, model monitoring, observability, production AI operations and AI lifecycle management.
- Proven experience managing cross-functional teams and large-scale delivery programs.
- Experience influencing architecture decisions and communicating with executive and business stakeholders.
- Preferred: Experience delivering enterprise AI programs in Banking, Financial Services, Insurance or other regulated industries.
- Preferred: Exposure to the Microsoft Copilot ecosystem and enterprise AI solutions.
- Preferred: Experience with Responsible AI, AI security and compliance frameworks.
- Preferred: Knowledge of Blockchain, Cloud Transformation, Digital Platforms or Enterprise Architecture frameworks.
- Candidates may qualify through equivalent experience across AI platform engineering, enterprise architecture, cloud transformation, machine learning engineering, data engineering or technical program delivery that demonstrates the required capabilities.
Day-to-Day Activities
- Define and review enterprise AI architectures covering data ingestion, LLM orchestration, RAG, vector databases, AI agents, integrations, security and monitoring.
- Collaborate with business leaders, product owners, architects, engineering, DevOps, MLOps, data and security teams to assess opportunities and guide delivery.
- Conduct
From Synechron Canada ULC_CANADA Company's careers page (Workday), checked 4 Nov 2026.
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Posted 3 Nov 2026 · checked 4 Nov 2026 · Report this job