What are the responsibilities and job description for the AI Architect position at Ascendum Solutions?
Seeking an AI Architect – Agentic Platforms to define the architectural foundations that power company’s enterprise agent ecosystem. This role is responsible for designing and governing the architecture for agent-based integrations, agent registries, scoring/evals infrastructure, grounding patterns, and multi-agent orchestration platforms. The AI Architect provides deep technical leadership across engineering, product, data science, security, and cloud teams to ensure that agents are built safely, consistently, and with enterprise-grade reliability, performance, and observability. This role combines expertise in large-scale AI systems, distributed cloud architecture, and modern agentic frameworks.
About the Role
- experience in cloud and distributed systems architecture focused on scalability, reliability, observability, and performance.
- designing enterprise AI/ML systems; 1 years hands-on with GenAI, agentic workflows, RAG, LLM-based integrations, or multi-agent systems.
- Strong expertise with agentic frameworks and tooling (MCP, LangChain, LangGraph,LlamaIndex, autogen, crewai, Agent sdk,OpenAI SDK etc).
- Hands-on experience in modern software development and engineering practices.
- Proven experience integrating APIs and enterprise systems into agentic platforms and workflows.
- Ability to rapidly build AI-driven prototypes, proofs of concept, and demo-ready product experiences.
- Experience defining and governing enterprise architecture standards, patterns, and reference architectures.
- Deep understanding of MCP servers, tool calling, registries, eval pipelines, agent observability, and multi-agent orchestration.
- Hands-on experience with Azure and GCP, including Kubernetes, containerization, identity, networking, CI/CD, and API platforms.
- Familiarity with AIOps/MLOps stacks (MLflow, model registries, vector DBs, semantic layers, feature stores, monitoring).
- Strong knowledge of security, compliance, risk, and Responsible AI (RAI) considerations for enterprise agent systems.
- Demonstrated ability to partner across engineering, data science, product, and security teams to deliver complex AI platform architectures.
Responsibilities
AI Agentic Platform Technical Leadership
- Define and evolve the enterprise reference architecture for AI agents, including orchestration frameworks, tool integration patterns, MCP servers, registries, and multi-agent coordination
- Design large-scale agent orchestration platforms that enable autonomous workflows across commerce, operations, and internal productivity domains
- Responsible for operational uptime adhering to SLAs, planning upgrades, rolling out new capabilities and integrations for agent platform.
- Establish grounding patterns using semantic layers, vector search, knowledge models, and Retrieval-Augmented Generation (RAG)
- Architect and develop systems that connect agents to trusted enterprise data, APIs, and business services
- Develop architectural patterns for safe, governed agent execution aligned with Responsible AI principles
Enterprise Platform Engineering Excellence
- Architect scalable, fault-tolerant AI agent platforms across hybrid cloud environments (Azure & GCP)
- Establish architecture standards ensuring low latency, high availability, resiliency, and observability.
- Partner with cloud and platform engineering teams to deliver containerized, API-driven, secure infrastructure for agent workloads
- Define platform lifecycle patterns including versioning, release gating, rollback strategies, and performance benchmarking
- Enable cost-efficient scaling of AI workloads across millions of enterprise and customer interactions
Agent Quality, Safety & Evaluation Innovation
- Define, develop and operationalize the Agentic SDLC, including evaluation frameworks, safety testing, regression gates, and release readiness criteria
- Architect systems for continuous agent improvement using automated evaluation pipelines and human feedback loops
- Establish enterprise standards for hallucination mitigation, prompt safety, PII protection, and AI misuse prevention
- Lead observability and AIOps patterns for agent monitoring, anomaly detection, and operational intelligence
- Define performance scoring frameworks for agent quality, reliability, and cost optimization
Strategic AI Platform Innovation
- Partner with engineering, product, and data science leaders to deliver intelligent agent platforms serving customer and enterprise use cases
- Drive innovation in multi-agent systems, LLM-powered workflows, and AI orchestration technologies
- Evaluate emerging agent frameworks, tooling, and open standards to guide platform strategy and build-vs-buy decisions
- Contribute to platform engineering excellence by building reusable AI infrastructure and developer enablement capabilities
- Provide architectural mentorship and technical guidance across teams on agentic AI design, scalable engineering practices, and enterprise AI standards