AI Architect (Agentic Process Automation):
Experience: 8+ Years (minimum 3 years focused on AI/ML, Generative AI, Agentic AI, Copilot, or Intelligent Automation solutions)
Location: India / Europe
Employment Type: Full-Time
About the Role:
We are seeking a hands-on AI Architect to design, govern, and scale enterprise-grade Agentic AI, Copilot, and Intelligent Automation solutions. The role combines AI architecture, enterprise data strategy, evaluation engineering, governance, and intelligent process automation to deliver secure, scalable, and business-driven AI solutions.
Key Responsibilities:
Architecture & Solution Design
Design and deliver enterprise-grade Agentic AI, Copilot, RAG, and Intelligent Automation solutions.
Architect multi-agent systems including planning, reasoning, memory management, workflow orchestration, tool integration, and Human-in-the-Loop controls.
Define enterprise AI strategy, reference architectures, standards, governance frameworks, best practices, and reusable accelerators.
Lead AI platform decisions spanning foundation models, agent frameworks, enterprise data, orchestration platforms, and integration patterns.
Architect and provide technical leadership for Python-based AI platforms, agent frameworks, automation services, APIs, and enterprise integrations.
Ensure AI solutions meet enterprise requirements for scalability, reliability, observability, security, compliance, and operational excellence.
Collaborate with product, engineering, security, data, and business stakeholders to identify high-value AI opportunities and deliver measurable business impact.
Conduct architecture reviews, support Architecture Review Boards (ARB), and oversee delivery across multiple AI initiatives.
Enterprise Integration, Data & AI Quality Engineering:
Integrate AI solutions with enterprise platforms including ERP, CRM, ITSM, HR, Finance, SharePoint, and other business systems.
Design AI architectures leveraging Microsoft Fabric, Databricks, Snowflake, Azure Data Lake, Enterprise Data Platforms, and enterprise knowledge repositories.
Architect secure integrations using APIs, Graph APIs, OAuth2, Function Calling, MCP, Agent-to-Agent communication, and event-driven architectures.
Design enterprise knowledge architectures across structured, semi-structured, and unstructured data sources.
Define data strategies covering knowledge sourcing, metadata management, chunking strategies, embedding architectures, vectorization, and retrieval optimization.
Design scalable data processing pipelines, orchestration services, and AI workloads using Python and cloud-native technologies.
Design and govern evaluation datasets, benchmark suites, golden test sets, and domain-specific validation frameworks.
Establish methodologies for measuring accuracy, groundedness, relevance, reasoning quality, latency, cost efficiency, and business outcomes.
Define evaluation pipelines for prompts, RAG systems, copilots, and autonomous agents using automated and human evaluation approaches.
Partner with business, domain, and data teams to establish trusted AI-ready data foundations, feedback loops, and continuous improvement processes.
Governance, Security & Reliability:
Implement Responsible AI practices covering privacy, security, compliance, auditability, risk management, ethical AI, and PII protection.
Define observability, testing, hallucination detection, resiliency, disaster recovery, model monitoring, and operational support strategies.
Establish AI quality gates, release criteria, evaluation standards, and governance processes for production AI systems.
Drive token governance, model routing, semantic caching, cost optimization, and mitigation strategies for bias, drift, and reliability risks.
Foundation Models & GenAI:
Provide technical leadership on LLM platforms including OpenAI GPT, Azure OpenAI, Anthropic Claude, Microsoft Copilot, and emerging foundation models.
Apply advanced Prompt Engineering, Context Engineering, Knowledge Grounding,…