The Solution Architect for AI-Enabled Software Solutions is responsible for designing scalable AI solutions, defining architecture patterns, collaborating with teams to integrate AI models, ensuring compliance and security, leading proof-of-concept developments, and staying updated with AI trends.
Solution Architect – AI-Enabled Software Solutions1
Key Responsibilities:
- Design and architect end-to-end AI-enabled solutions that align with busiess objectives.
- Define scalable, secure, and high-performance architecture patterns for AI-driven applications.
- Collaborate with data scientists, ML engineers, and product teams to integrate AI/ML models into software solutions.
- Select appropriate cloud platforms, frameworks, and AI technologies to optimize performance and cost-efficiency.
- Ensure AI-driven observability, monitoring, and security in solution designs.
- Provide technical leadership, mentoring developers and engineers on AI solution best practices.
- Define and enforce governance, compliance, and security standards for AI-enabled architectures.
- Lead proof-of-concept (PoC) and prototype development to validate AI capabilities.
- Evaluate and recommend third-party AI tools, APIs, and platforms.
- Collaborate with stakeholders, clients, and executive leadership to align AI solutions with business strategy.
- Stay updated with emerging AI trends, cloud advancements, and industry best practices.
Key Qualifications:
- 10+ years of experience in software architecture, with a strong focus on AI-enabled solutions.
- Expertise in AI/ML frameworks such as TensorFlow, PyTorch, Hugging Face, or OpenAI APIs.
- Strong proficiency in on prem (like Linux) and cloud platforms (for example AWS, Azure, GCP) and AI/ML services (SageMaker, Vertex AI, Azure AI).
- Experience with big data processing (example - Spark, Databricks, Kafka) and AI-driven analytics.
- Hands-on experience in designing microservices, APIs, and event-driven architectures.
- Strong understanding of MLOps, CI/CD for AI models, and AI model lifecycle management.
- Experience with AI observability, monitoring, and responsible AI governance.
- Proficiency in programming languages like Python, Java.
- Strong understanding of enterprise security, compliance, and data privacy regulations.
Preferred Skills:
- Knowledge of LLMs, generative AI, and multi-agent AI architectures.
- Experience integrating AI-powered automation and intelligent decision-making into enterprise applications.
- Hands-on experience with AI-driven DevOps, Infrastructure as Code (IaC), and containerization (Docker, Kubernetes).
- Familiarity with Edge AI, federated learning, and hybrid AI deployment models.
Top Skills
Ai/Ml Frameworks
APIs
AWS
Azure
Azure Ai
Big Data Processing
Databricks
Docker
GCP
Hugging Face
Java
Kafka
Kubernetes
Microservices
Openai Apis
Python
PyTorch
Sagemaker
Spark
TensorFlow
Vertex Ai
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