AI Solutions Architect

Job Description

We are seeking an experienced AI Solutions Architect to design, develop, and implement enterprise-scale Artificial Intelligence solutions that accelerate digital transformation and drive business innovation. The ideal candidate will architect AI platforms leveraging Generative AI, Large Language Models (LLMs), Machine Learning, Retrieval-Augmented Generation (RAG), cloud-native AI services, and modern data platforms.

In this role, you will collaborate with business leaders, data scientists, software engineers, cloud architects, and cybersecurity teams to translate business requirements into secure, scalable, and production-ready AI solutions. You will define AI architecture standards, integrate AI services across enterprise systems, and ensure compliance with Responsible AI and data governance practices.

Key Responsibilities

• Design enterprise AI solution architectures using cloud-native AI platforms.
• Lead the architecture and deployment of Generative AI and LLM-powered applications.
• Develop scalable Retrieval-Augmented Generation (RAG) solutions using vector databases and knowledge repositories.
• Design AI workflows for chatbots, intelligent assistants, document processing, recommendation systems, and predictive analytics.
• Integrate AI solutions with enterprise applications, APIs, databases, and cloud services.
• Select appropriate foundation models, embedding models, and orchestration frameworks for business use cases.
• Define AI governance, security, scalability, and Responsible AI standards.
• Collaborate with Data Engineering teams to build AI-ready data pipelines and feature stores.
• Implement AI model deployment, monitoring, versioning, and lifecycle management using MLOps best practices.
• Optimize AI performance, latency, cost, and inference efficiency.
• Conduct architecture reviews and provide technical leadership across AI initiatives.
• Evaluate emerging AI technologies and recommend enterprise adoption strategies.
• Mentor AI engineers, developers, and solution architects on best practices.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field.
• 8+ years of experience in software engineering, cloud architecture, AI, or machine learning.
• 3+ years of experience designing enterprise AI or Generative AI solutions.
• Strong knowledge of Large Language Models (LLMs), Prompt Engineering, RAG, and AI orchestration frameworks.
• Experience with cloud AI platforms such as Microsoft Azure AI, AWS Bedrock, or Google Vertex AI.
• Proficiency in Python, REST APIs, SQL, and microservices architecture.
• Experience with containerization (Docker) and orchestration (Kubernetes).
• Strong understanding of AI security, governance, privacy, and compliance.
• Excellent communication, stakeholder management, and technical leadership skills.

Preferred Skills

• Generative AI
• Large Language Models (LLMs)
• OpenAI GPT Models
• Anthropic Claude
• Google Gemini
• Microsoft Azure AI Foundry
• AWS Bedrock
• Google Vertex AI
• LangChain
• LlamaIndex
• Semantic Kernel
• Hugging Face Transformers
• Vector Databases (Pinecone, Weaviate, Milvus, ChromaDB)
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Python
• SQL
• Docker
• Kubernetes
• MLflow
• Apache Spark
• Azure Data Factory
• Databricks
• Git
• CI/CD
• Terraform

Preferred Certifications

• Microsoft Certified: Azure AI Engineer Associate (AI-102)
• Microsoft Certified: Azure Solutions Architect Expert (AZ-305)
• AWS Certified Machine Learning – Specialty
• AWS Certified Solutions Architect – Professional
• Google Professional Machine Learning Engineer
• Google Professional Cloud Architect
• Databricks Certified Machine Learning Professional
• Kubernetes Certified Application Developer (CKAD)

Benefits

• Competitive salary with annual performance bonus
• Medical, dental, and vision insurance
• Flexible remote and hybrid work options
• Paid vacation and company holidays
• Employee stock purchase program (where applicable)
• Professional certification reimbursement
• Learning and development budget
• Career advancement opportunities in AI and cloud technologies