Solution Architect – AI-Enabled Software Solutions

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.