AI LLM Architect

YOU ARE 

As an experienced and Senior AI/LLM  Architect, you will play a pivotal role in designing and delivering end-to-end AI platform architectures that power the modern, reinvented enterprise. Operating at the intersection of business and engineering, you will own the technical design of advanced AI systems spanning classical machine learning, generative AI, and agentic systems ensuring they are purposefully architected to meet client business objectives and enterprise-grade standards.    Within this scope, you will take deep ownership of one or more critical architecture domains such as agentic application design, AI security and trust, AI operations and observability, data and knowledge engineering, or model platforms and inference serving as the lead authority in your domain across client engagements.

THE WORK 

• Translate business strategy into a technical vision by defining the non-functional requirements (NFRs) necessary to meet operational goals for performance, reliability, and cost. 

• Lead stakeholder workshops to align on technical feasibility, define project scope, and manage expectations with clients and leadership. 

• Drive the technology selection process, evaluating build-vs-buy decisions for AI platforms (e.g., Arize, LangSmith) and foundational models. 

• Architect model- and tool-agnostic multi-agent systems governed by an MCP Control Plane. 

• Design and implement the Agent Registry as the mandatory system of record and the AI Gateway for runtime policy enforcement. 

• Design and implement a certification gate to ensure no uncertified agents enter production, validating identity, policies, and evaluation metrics. 

• Design, implement, and abstract core agent services, including a first-class abstracted memory service with semantic, episodic, and procedural endpoints. 

• Architect the end-to-end data pipeline for AI systems, including data ingestion, preprocessing, and synchronization for fine-tuning and RAG. 

• Design and implement the context layer—spanning knowledge graphs, vector search, and semantic retrieval—to create reliable, grounded RAG pipelines. 

• Architect foundation model adaptation strategies, including dynamic, cost-and-performance-aware model routing and selection. 

• Design, implement, and prototype high-throughput, low-latency inferencing solutions using techniques like response caching and request batching. 

• Define security, governance, and observability as centrally-enforced, by-design controls for all AI systems. 

• Architect a robust, defense-in-depth security framework, including per-agent identity with IAM/IAP binding and layered guardrails. 

• Design and implement FinOps controls enforced at the AI Gateway, including token budgets, cost-center labeling, and threshold alerts. 

• Establish the framework for comprehensive system evaluation, adopting productized tools and instrumenting observability with OTel 

• Define and maintain the enterprise-wide AI reference architecture, reusable design patterns, and a library of approved software components. 

• Independently design, implement, build, and deliver proof-of-concept prototypes and foundational software components to validate architectural decisions. 

• Produce and own authoritative architecture artifacts, including blueprints, sequence diagrams, design specifications, and Architectural Decision Records (ADRs). 

• Mentor and guide cross-functional engineering teams (data, ML, application) on architectural best practices and design patterns. 

• Continuously research and integrate emerging AI patterns, frameworks, and technologies to maintain a forward-looking architecture. 

EDUCATION 

• Bachelor's Degree or equivalent 

BASIC (REQUIRED) QUALIFICATION 

• Minimum of 2 years of experience in designing & deploying enterprise grade advanced ai solutions using agentic, generative and classical AI/ML using at least one cloud vendor. 

• Minimum of 2 years of experience in the Agentic, LLM and Generative AI space. 

• Minimum of 4 years of coding experience using python 

• Minimum of 2 years of experience architecting and operationalizing LLM driven application architecture patterns. 

• Minimum of 4 years in coding engineering, machine learning, deep learning and NLP solutions and applications. 

• Minimum of 4 years of experience as a machine learning architect in the industry designing big data, machine learning. large scale analytical engineering solutions.