AI Architect

Job Summary:

We are seeking an experienced AI Architect to lead the design, development, and implementation of enterprise-scale AI and ML solutions. The ideal candidate will architect end-to-end AI systems, integrate LLMs and GenAI capabilities, define data and model pipelines, and guide engineering teams in delivering production-ready AI solutions. This role requires deep technical expertise, strong architectural leadership, and hands-on experience with cloud-based AI platforms.

Key Responsibilities:

• Architect, design, and deliver enterprise AI/ML solutions across cloud and on-premise environments.
• Lead solution design for LLMs, Generative AI, NLP, RAG pipelines, and agent-based AI systems.
• Define AI architecture blueprints, model lifecycle workflows, and integration patterns.
• Oversee data ingestion, feature pipelines, vector search, and model deployment workflows.
• Evaluate and select AI frameworks, libraries, cloud services, and MLOps tools.
• Collaborate with data engineering, product, and platform teams to ensure scalable and secure AI systems.
• Guide teams in implementing MLOps, CI/CD for ML, monitoring, and observability.
• Conduct architecture reviews, ensure adherence to standards, and manage governance requirements.
• Develop POCs, reference architectures, and reusable AI components.
• Drive best practices for responsible AI, model transparency, and data privacy.
  

Required Qualifications:

• 12+ years of experience in AI/ML engineering, data engineering, or solution architecture.
• Strong hands-on expertise in Python, ML frameworks (TensorFlow, PyTorch), and LLM/GenAI ecosystems.
• Experience designing RAG, embeddings, vector databases, and LLM-powered applications.
• Strong understanding of cloud AI services (AWS, Azure, GCP).
• Experience with MLOps, CI/CD for ML, Model Registry, monitoring, and deployment pipelines.
• Understanding of security, compliance, and architectural governance.
• Excellent communication and stakeholder-management skills.
  

Preferred Skills:

• Experience with agent frameworks (LangChain, AutoGen, CrewAI).
• Familiarity with Databricks, Spark, and large-scale data processing.
• Experience with knowledge graphs or enterprise search systems.
• Understanding of microservices, API gateways, and system integration patterns.