ARTIFICIAL INTELLIGENCE (AI) SPECIALIST

Job Description

We are seeking a highly accomplished Principal AI Architect to lead the design, architecture, and governance of enterprise-scale Artificial Intelligence (AI), Machine Learning (ML), Generative AI (GenAI), and Agentic AI platforms. This role is responsible for defining AI strategy, establishing architecture standards, driving innovation, and delivering mission-critical AI solutions that create measurable business impact.

The ideal candidate will have 8–10 years of experience in software development and enterprise solution architecture, including at least 4 years of hands-on experience in AI, ML, and GenAI technologies. The candidate should possess deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Multi-Agent Systems, and AI orchestration frameworks.

Strong experience with Microsoft Azure and AWS cloud platforms is required, including services such as Azure OpenAI, Azure AI Foundry, Azure AI Search, AWS Bedrock, and Amazon SageMaker. The role requires the ability to architect scalable, secure, and cloud-native AI solutions while providing technical leadership to engineering, data science, and product teams.

The Principal AI Architect will define enterprise AI reference architectures, establish governance and best practices, mentor technical teams, evaluate emerging technologies, and drive AI adoption across the organization.

Job Requirements

·      Master's or Ph.D. in Artificial Intelligence& Machine Learning from a recognized institution.

·      8–10+ years of experience in enterprise software development, distributed systems architecture, and cloud-native application design.

·      Minimum 4–6 years of hands-on experience in AI, Machine Learning, and Generative AI solution architecture.

·      Demonstrated experience architecting and deploying enterprise-scale AI platforms serving mission-critical workloads.

·      Deep expertise in Large Language Models (LLMs), Small Language Models (SLMs), Retrieval-Augmented Generation (RAG), Agentic AI, Multi-Agent Systems, Knowledge Graphs, and Autonomous Decisioning Systems.

·      Strong understanding of advanced AI concepts including model fine-tuning, reinforcement learning, synthetic data generation, prompt optimization, evaluation frameworks, model alignment, hallucination mitigation, and AI safety.

·      Hands-on experience with agent orchestration frameworks such as LangGraph, AutoGen, Semantic Kernel, CrewAI, Microsoft Agent Framework, or equivalent technologies.

·      Experience designing AI systems leveraging MCP (Model Context Protocol), A2A (Agent-to-Agent) communication protocols, event-driven architectures, and distributed agent ecosystems.

·      Proven expertise in vector databases, semantic search, graph databases, knowledge engineering, and enterprise RAG architectures.

·      Experience implementing AI observability, governance, security, auditability, explainability, and responsible AI controls in regulated environments.

·      Strong expertise in Microsoft Azure and AmazonWeb Services (AWS), including Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Kubernetes Service (AKS), Azure Functions, AWS Bedrock, Amazon Sage Maker, Open Search, Lambda, ECS/EKS, and cloud-native AI platform architecture.

·      Strong proficiency with Kubernetes, Docker, Terraform, Infrastructure as Code (IaC), and enterprise DevSecOps practices.

·      Experience defining enterprise AI reference architectures, AI operating models, and organization-wide AI adoption strategies.

·      Proven ability to lead cross-functional teams consisting of architects, AI engineers, data scientists, platform engineers, and business stakeholders. Strong understanding of MLOps, LLMOps, model lifecycle management, continuous evaluation, and AI production monitoring.

·      Experience presenting AI strategy and architecture recommendations to executive leadership and steering committees.

Preferred Qualifications

·      Experience building Agentic AI platforms, AI Command Centers, or Autonomous Enterprise Systems.

·      Contributions to AI research, patents, technical publications, open-source projects, or industry standards.

·      Experience designing AI systems with human-in-the-loop (HITL), confidence calibration, self-healing workflows, and autonomous remediation capabilities.

·      Expertise in AI performance benchmarking, token optimization, reasoning evaluation, and cost governance for large-scale AI deployments.

·      Professional certifications such as Microsoft Azure Solutions Architect Expert, Azure AI Engineer Associate, AWS Solutions Architect Professional, AWS Machine Learning Specialty, or equivalent cloud AI certifications.

Key Responsibilities

·      Define and own the enterprise AI architecture vision, standards, governance model, and technology roadmap.

·      Architect next-generation Agentic AI platforms capable of autonomous planning, reasoning, execution, learning, and collaboration across Azure and AWS cloud environments.

·      Design and govern enterprise AI platforms leveraging Azure OpenAI, Azure AI Foundry, Azure AI Search, AWS Bedrock, Amazon Sage Maker, and related cloud-native AI services.

·      Establish enterprise patterns for AI observability, resilience, fault isolation, model evaluation, security, and continuous improvement.

·      Lead architecture reviews and provide technical oversight for all AI initiatives across the organization.

·      Drive innovation through the evaluation and adoption of emerging AI technologies, cloud services, frameworks, and architectural patterns.

·      Partner with senior leadership to shape the organization's long-term AI transformation strategy and cloud AI adoption roadmap.