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 architecture reviews, technical governance sessions, solution-quality assessments, risk reviews and program-planning discussions.
- Make or facilitate architecture and delivery decisions within the established governance framework, while mentoring teams and ensuring production solutions meet scalability, performance, security and compliance expectations.
Qualifications
- A bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering or a related field is required or preferred based on applicable experience.
- An MBA or equivalent leadership qualification is an added advantage.
- Certifications in Azure AI, AWS Machine Learning, cloud architecture, enterprise architecture or equivalent technologies are preferred.
- Training in Generative AI, AI governance, responsible AI, AI security, MLOps, cloud-native architecture and program delivery is preferred.
- Commitment to continuous professional development in AI architecture, LLM ecosystems, Agentic AI, cloud platforms, enterprise integration, governance and emerging AI technologies is expected.
Professional Competencies
- Applies systems thinking and structured problem-solving to evaluate complex AI architecture, integration, data, security, scalability and operational challenges.
- Provides technical leadership through clear standards, architecture guidance, mentoring, constructive review and coordinated decision-making across multidisciplinary teams.
- Communicates AI strategy, architecture choices, technical risks, delivery priorities and business value clearly to executive, business and technical stakeholders.
- Adapts to evolving AI technologies, foundation models, cloud services, governance requirements and organizational priorities while maintaining architectural consistency.
- Identifies practical opportunities for Generative AI adoption, platform reuse, automation, responsible innovation and sustainable technology delivery.
- Manages competing priorities, program dependencies, delivery milestones, risks and resources while maintaining focus on measurable business and technology outcomes.
SYNECHRON’S DIVERSITY & INCLUSION STATEMENT
Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.
All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.
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