What are the responsibilities and job description for the Solutions Architecture Lead (Apps Dev Manager) position at Expert Technology Services?
Job Summary (List Format) – Solution Architecture Manager
- Location & Schedule: Hybrid (4 days onsite per week), local candidates only.
- Role Overview: Serve as a strategic technical leader, bridging enterprise architecture with advanced AI/ML technologies.
- System Oversight: Lead the development of modern, scalable software systems with AI as a core component.
- Enterprise & Cloud Architecture: Deep expertise in distributed systems, microservices, API-first design, and major cloud platforms (Salesforce, Azure, GCP, including AI/ML stacks).
- AI/ML Ecosystems: Strong knowledge of AI lifecycle, LLMs/SLMs, NLP, computer vision, vector databases, RAG architectures.
- Data Engineering & MLOps: Understanding of CI/CD for ML, data pipelines, data lakes/warehouses, and model lifecycle management.
- Security, Privacy, & Ethics: Knowledge of data privacy regulations (e.g., CCPA), AI compliance (NIST AI RMF, EU AI Act), and methods to mitigate AI bias and risks.
- Architecture Frameworks: Familiarity with frameworks like TOGAF, Zachman, and Agile/Scrum methodologies.
- Cloud Cost Management: Understanding of FinOps, cost structures of AI compute (GPU/TPU), and API token-based pricing.
- Business-Technical Translation: Ability to convert business needs into effective AI and technical solutions.
- Executive Communication: Ability to clearly explain complex AI concepts and ROI to C-level executives and the board.
- Risk Management: Proactively anticipate and mitigate technical, security, and ethical risks in AI deployments.
- Adaptability: Stay current with rapid AI advancements and integrate new research into enterprise systems.
- Cross-Functional Leadership: Influence and coordinate across multiple teams (product, legal, security, engineering) to ensure successful AI service deployment.
- System Design & Integration: Design scalable, secure architectures that embed AI into enterprise and legacy systems.
- Technical Evaluation: Assess AI vendors, open-source models, and APIs to guide build vs. buy decisions.
- Team Leadership: Manage, mentor, and evaluate technical teams (cloud architects, ML engineers, data scientists, developers).
- Strategic Roadmapping: Develop and maintain a long-term AI/architecture roadmap aligned with business goals.
- Budget & Vendor Management: Negotiate contracts, manage vendors, and optimize budgets for software and projects.
- Technical Skills: Proficient in high-level programming languages (Python, SQL, Java, Go) for architecture review and complex troubleshooting.
- Required Education: Bachelor’s degree in Computer Science or Computer Engineering.
- Location & Schedule: Hybrid (4 days onsite per week), local candidates only.
- Role Overview: Serve as a strategic technical leader, bridging enterprise architecture with advanced AI/ML technologies.
- System Oversight: Lead the development of modern, scalable software systems with AI as a core component.
- Enterprise & Cloud Architecture: Deep expertise in distributed systems, microservices, API-first design, and major cloud platforms (Salesforce, Azure, GCP, including AI/ML stacks).
- AI/ML Ecosystems: Strong knowledge of AI lifecycle, LLMs/SLMs, NLP, computer vision, vector databases, RAG architectures.
- Data Engineering & MLOps: Understanding of CI/CD for ML, data pipelines, data lakes/warehouses, and model lifecycle management.
- Security, Privacy, & Ethics: Knowledge of data privacy regulations (e.g., CCPA), AI compliance (NIST AI RMF, EU AI Act), and methods to mitigate AI bias and risks.
- Architecture Frameworks: Familiarity with frameworks like TOGAF, Zachman, and Agile/Scrum methodologies.
- Cloud Cost Management: Understanding of FinOps, cost structures of AI compute (GPU/TPU), and API token-based pricing.
- Business-Technical Translation: Ability to convert business needs into effective AI and technical solutions.
- Executive Communication: Ability to clearly explain complex AI concepts and ROI to C-level executives and the board.
- Risk Management: Proactively anticipate and mitigate technical, security, and ethical risks in AI deployments.
- Adaptability: Stay current with rapid AI advancements and integrate new research into enterprise systems.
- Cross-Functional Leadership: Influence and coordinate across multiple teams (product, legal, security, engineering) to ensure successful AI service deployment.
- System Design & Integration: Design scalable, secure architectures that embed AI into enterprise and legacy systems.
- Technical Evaluation: Assess AI vendors, open-source models, and APIs to guide build vs. buy decisions.
- Team Leadership: Manage, mentor, and evaluate technical teams (cloud architects, ML engineers, data scientists, developers).
- Strategic Roadmapping: Develop and maintain a long-term AI/architecture roadmap aligned with business goals.
- Budget & Vendor Management: Negotiate contracts, manage vendors, and optimize budgets for software and projects.
- Technical Skills: Proficient in high-level programming languages (Python, SQL, Java, Go) for architecture review and complex troubleshooting.
- Required Education: Bachelor’s degree in Computer Science or Computer Engineering.