Large Language Model Architect

Large Language Model Architect

Project Role : Large Language Model Architect
Project Role Description : Architect large language models (LLM) that can process and generate natural language. Design neural network parameters, trained on large quantities of unlabeled text data.
Must have skills : Microsoft Azure Data Services
Good to have skills : Large Language Models (LLMs)
Minimum 7.5 year(s) of experience is required
Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent
Engineer role in AI LLM Technology Architecture. Hands-on engineering role focused on designing, building, integrating, testing and operationalizing enterprise-grade LLM, GenAI and agentic AI components across active client engagements.
Own platform-specific engineering on Azure, translating high-level architecture into working, production-quality components for LLM-driven applications, RAG pipelines, multi-agent workflows and AI platform integrations.
Bring practical industry experience in banking, insurance, healthcare, public sector, retail or energy to identify domain data, process constraints, controls and adoption risks while designing GenAI solutions that are safe, scalable and relevant.
Operate as a hands-on technical lead or engineering lead, contributing code, design decisions, reusable patterns and engineering documentation.

Key Responsibilities
Design and build LLM application components including prompts, tools, agents, orchestration flows, memory/context handling, retrieval pipelines and evaluation harnesses.
Create Foundry-based agents with enterprise tools, knowledge and memory integrate Azure AI Search for RAG implement secure API/tool calls through API Management, Logic Apps and Functions instrument traces, evaluations, guardrails and policy controls for production agents.
Implement data ingestion, parsing, chunking, enrichment, embeddings, vector search and retrieval workflows for structured and unstructured enterprise content.
Engineer safety and control components including PII detection/redaction, prompt-injection defenses, content filters, guardrails, authentication, authorization, lineage and audit logging.
Collaborate with architects, data engineers, product owners and security stakeholders to convert solution designs into tested, observable and maintainable software components.
Maintain technical artifacts such as component designs, integration specifications, deployment runbooks, evaluation results and reusable engineering patterns.

Required Qualifications
Bachelor s degree in Computer Science, Computer Engineering, Data Science, AI/ML, Information Technology or a related engineering discipline.
Hands-on coding experience in Python and strong understanding of APIs, distributed systems, CI/CD, testing, observability and secure SDLC practices.
Experience delivering AI/ML or data products in at least one industry domain such as banking, insurance, healthcare, public sector, retail or energy.

Required Skills/ Experience
Hands-on experience with Microsoft Foundry / Azure AI Foundry, Foundry Agent Service, Azure OpenAI models, Azure AI Search, Prompt Flow, Azure Functions, Logic Apps, API Management, Event Grid, Microsoft Fabric, Entra ID, Key Vault, Azure Monitor and Application Insights.
Strong understanding of LLM application architecture patterns including RAG, function/tool calling, agent orchestration, model invocation, prompt engineering, embeddings, vector databases and evaluation metrics.
Ability to implement traditional ML and GenAI components across ingestion, feature/data preparation, model integration, deployment, monitoring and continuous improvement.
Practical knowledge of security, privacy, governance, performance, scalability, reliability and cost controls for production AI systems.
Experience with Git-based development, automated testing, CI/CD pipelines, infrastructure-as-code and agile delivery in client-facing environments.

Good to Have Skills
Azure AI Engineer, Azure Solutions Architect or Azure Developer certification experience with Microsoft Fabric, Semantic Kernel, Prompt Flow, private networking, Defender for Cloud and enterprise Responsible AI governance.
Exposure to open-source frameworks such as LangChain, LangGraph, LlamaIndex, Haystack, MLflow, FastAPI, Docker and Kubernetes.
Experience with Responsible AI, model risk management, synthetic data generation, human-in-the-loop review, A/B testing and GenAI cost optimization.