AI Solutions Architect

Job Summary

We are seeking an experienced AI Solutions Architect with 12+ years of IT experience and strong expertise in designing, developing, and implementing enterprise-scale Artificial Intelligence and Machine Learning solutions. The ideal candidate will lead AI strategy, define scalable architectures, integrate Generative AI technologies, and collaborate with business and technical teams to deliver innovative AI-driven solutions across cloud platforms.

Required Qualifications

• 12+ years of overall IT experience with at least 5+ years in AI/ML architecture and solution design.
• Bachelor''s or Master''s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
• Strong experience designing enterprise AI solutions using cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, Prompt Engineering, and Generative AI applications.
• Expertise in Python and AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers, LangChain, and LlamaIndex.
• Experience with vector databases such as Pinecone, FAISS, ChromaDB, Weaviate, or Milvus.
• Strong knowledge of MLOps, model deployment, CI/CD pipelines, Docker, Kubernetes, and ML lifecycle management.
• Experience working with REST APIs, microservices, and distributed architectures.
• Knowledge of SQL, NoSQL databases, data engineering concepts, and cloud-native services.
• Experience with Azure OpenAI, OpenAI APIs, Anthropic Claude, Google Gemini, or similar AI platforms.
• Strong understanding of Responsible AI, AI Governance, model security, compliance, and ethical AI principles.
• Excellent communication, stakeholder management, and leadership skills.

Technical Skills

• Programming: Python, SQL, Java, Scala (preferred)
• AI/ML: TensorFlow, PyTorch, Scikit-learn, Hugging Face
• Generative AI: OpenAI, Azure OpenAI, Claude, Gemini, LangChain, LlamaIndex
• Vector Databases: Pinecone, ChromaDB, FAISS, Weaviate, Milvus
• Cloud: AWS, Azure, Google Cloud Platform
• MLOps: MLflow, Kubeflow, SageMaker, Azure ML
• Containerization: Docker, Kubernetes
• Databases: PostgreSQL, MongoDB, Snowflake, Redis
• DevOps: Git, Jenkins, Azure DevOps, GitHub Actions
• Architecture: Microservices, Event-Driven Architecture, REST APIs

Roles & Responsibilities

• Design and implement enterprise AI and Generative AI solution architectures.
• Lead AI transformation initiatives and define AI roadmaps aligned with business objectives.
• Architect scalable LLM-based applications using RAG, vector databases, embeddings, and AI agents.
• Collaborate with business stakeholders to gather requirements and translate them into AI-driven solutions.
• Design secure, scalable, and high-performance AI platforms on AWS, Azure, or Google Cloud Platform.
• Develop AI reference architectures, best practices, governance standards, and reusable frameworks.
• Lead the deployment and monitoring of AI models using MLOps best practices.
• Optimize model performance, latency, scalability, and operational efficiency.
• Integrate AI solutions with enterprise applications through APIs and cloud services.
• Ensure compliance with security, privacy, Responsible AI, and regulatory requirements.
• Mentor development teams and provide technical leadership throughout project execution.
• Evaluate emerging AI technologies and recommend innovative solutions to improve business outcomes.

Preferred Qualifications

• Experience with Agentic AI frameworks and autonomous AI systems.
• Knowledge of Graph RAG, Knowledge Graphs, and Semantic Search.
• Experience with enterprise AI governance frameworks.
• Cloud certifications (AWS Solutions Architect, Azure AI Engineer, Google Professional ML Engineer) are highly preferred.
• TOGAF or enterprise architecture certification is a plus.
• Experience working in Banking, Healthcare, Insurance, Retail, or Financial Services domains is preferred.