Job Description:
We are seeking a AI Solutions Architect to join our team. The ideal candidate will have a background in artificial intelligence and software architecture, with knowledge of designing and implementing AI-driven solutions that address business needs. As an AI Solutions Architect, you will play a pivotal role in leading the design and development of AI systems, guiding technical teams, and collaborating with stakeholders to deliver impactful solutions.
Responsibilities:
- Lead the design and architecture of AI solutions, including system integration, data flows, and technology stack selection, to meet business requirements and objectives.
- Collaborate with cross-functional teams, including data scientists, engineers, and product managers, to define solution architecture, technical requirements, and implementation strategies.
- Evaluate and recommend AI technologies, frameworks, and tools based on project requirements, performance considerations, and industry best practices.
- Design scalable and extensible AI architectures that support real-time processing, high availability, and robustness, considering factors such as data security, privacy, and compliance.
- Develop proof-of-concept prototypes and architecture diagrams to demonstrate the feasibility and effectiveness of proposed solutions to stakeholders.
- Provide technical leadership, guidance, and mentorship to development teams, ensuring adherence to architectural principles, coding standards, and best practices.
- Collaborate with sales and marketing teams to support pre-sales activities, including solution demonstrations, customer presentations, and proposal development.
- Stay updated on emerging trends, technologies, and advancements in AI and related fields, and provide thought leadership within the organization.
- Associates, Bachelor's degree in Computer Science, Engineering, or related field. Advanced degree (Master's or Ph.D.) preferred.
- Experience in software architecture, design, and development, with a focus on AI/ML solutions.
- Proven experience architecting and implementing AI-driven applications, such as predictive analytics, recommendation systems, or computer vision solutions.
- Deep understanding of AI/ML algorithms, techniques, and frameworks, including TensorFlow, PyTorch, scikit-learn, and others.
- Proficiency in programming languages such as Python, Java, or C++, as well as experience with cloud platforms and services (e.g., AWS, Azure, Google Cloud).
- Strong analytical and problem-solving skills, with the ability to translate business requirements into technical solutions.
- Excellent communication and interpersonal skills, with the ability to effectively collaborate with technical and non-technical stakeholders.
- Leadership abilities, including the ability to inspire and motivate teams, foster innovation, and drive results in a dynamic and fast-paced environment.
- AWS Certified Solutions Architect - Associate or Professional
- Microsoft Certified: Azure Solutions Architect Expert
- Google Cloud Certified - Professional Cloud Architect
- IBM Certified Architect - AI Engineer
- Business Acumen: Understanding of business processes, industry trends, and customer needs is essential for aligning AI solutions with strategic objectives and delivering value to stakeholders.
- Project Management: Proficiency in project management methodologies, tools, and techniques can help in effectively planning, executing, and managing AI projects from inception to deployment, ensuring timely delivery and successful outcomes.
- Data Engineering: Knowledge of data engineering principles, including data ingestion, transformation, storage, and governance, can facilitate the integration of diverse data sources and the creation of robust data pipelines to support AI applications.
- Performance Optimization: Experience in optimizing AI models and algorithms for performance, efficiency, and scalability, including techniques such as parallel processing, distributed computing, and hardware acceleration, can enhance the effectiveness and responsiveness of AI solutions.
- Regulatory Compliance: Awareness of regulatory frameworks and compliance requirements related to data privacy, security, and ethical considerations, such as GDPR, HIPAA, and AI ethics guidelines, is important for ensuring that AI solutions adhere to legal and ethical standards.
- Technical Writing: Proficiency in technical writing and documentation skills can facilitate the creation of architecture documents, design specifications, and technical documentation to communicate complex concepts and requirements effectively to stakeholders.
- Presentation Skills: Strong presentation and storytelling skills are essential for effectively communicating architectural designs, solution concepts, and project plans to technical and non-technical audiences, including executives, customers, and partners.
- Collaborative Problem-solving: Ability to collaborate with multidisciplinary teams, including data scientists, engineers, business analysts, and domain experts, to identify, analyze, and solve complex problems and challenges encountered during the development and deployment of AI solutions.
- Change Management: Experience in change management principles and practices can help in managing organizational change and adoption of AI technologies, including training, communication, and stakeholder engagement, to ensure successful implementation and user acceptance.
- Continuous Learning: Commitment to continuous learning and staying updated on emerging technologies, methodologies, and best practices in AI, machine learning, software architecture, and related fields is crucial for remaining at the forefront of innovation and driving continuous improvement in AI solutions architecture.
