AI LLM Architecture Senior Analyst

About the Role

As a hands-on AI Engineer , you will be at the heart of designing and developing the building blocks that power advanced AI systems for modern enterprises. This is a highly technical, implementation-focused engineering role where you will spend most of your time designing, developing, integrating, and testing AI system components across traditional machine learning, generative AI, and agentic systems as part of active client engagements.

You will transform detailed architectures and design specifications into production-ready software components. This includes writing clean, maintainable code, making low-level design decisions within your area of responsibility, and ensuring your components integrate reliably into broader AI systems.

You will build and assemble various AI agent system components, including:

• Individual agent logic
• Tool integrations
• Skills and capability components
• Memory components

You will also contribute to the development and integration of foundation models and traditional machine learning models into end-to-end AI pipelines.

A practical curiosity for the open-source ecosystem is essential. You will continuously evaluate, learn, and adopt relevant libraries and frameworks covering areas such as:

• Agent orchestration
• Vector storage
• Model serving
• Machine Learning pipelines

You will select and apply the most appropriate technologies for each business challenge. Additionally, you will configure, integrate, and deploy third-party AI technologies and platform services, developing a solid understanding of their capabilities and limitations to effectively leverage them within large-scale enterprise environments.

You will design enterprise-grade components that meet requirements for:

• Security
• Observability
• Governance
• Performance
• Scalability

You will create and maintain technical deliverables related to your engineering work, including:

• Low-level design documents
• Component specifications
• Integration contracts

This ensures that your developments are well documented, testable, and easily transferable.

You will work as a technical contributor within multidisciplinary teams comprising Data Engineers, ML Engineers, and Application Developers. You will operate under the guidance of Lead Architects and Principal Architects while actively contributing to technical problem-solving and design discussions within your area of expertise.

This role represents an excellent opportunity to develop deep hands-on expertise across the AI Engineering stack, strengthen your software engineering capabilities for cutting-edge AI systems, and progressively grow into a Lead Engineer or Architect position.

Key Responsibilities

AI Agents and Multi-Agent Systems

• Design, develop, and configure individual agents, including their prompts, tools, and skills, and integrate them into multi-agent workflows.
• Implement agent orchestration logic to manage task handoffs, communication, and error recovery.
• Develop prompt assembly logic and manage the information provided to models within their context windows.
• Implement memory components that store and retrieve conversational history and relevant context.

Foundation Models and Fine-Tuning

• Integrate foundation models into applications by selecting the most appropriate model and invocation strategy for each use case.
• Design and execute fine-tuning pipelines, including data preparation and model training, to adapt models for specific business domains.
• Apply a strong understanding of Transformer-based architectures.

Retrieval-Augmented Generation (RAG)

• Develop ingestion pipelines capable of parsing, chunking, enriching, and indexing unstructured enterprise content for search and retrieval purposes.
• Implement embedding generation, integrate vector databases, and develop retrieval components, including connectors and adapters required to support end-to-end RAG (Retrieval-Augmented Generation) pipelines.

Evaluation and Quality Assurance

• Design evaluation frameworks and test suites to measure agent and component quality using metrics such as: Accuracy Relevance Faithfulness
• Share findings and recommendations to drive continuous design improvements.

Security, Governance, and Compliance

• Implement guardrails, content filtering mechanisms, and protections against prompt injection attacks.
• Develop PII (Personally Identifiable Information) detection and redaction components.
• Integrate access-control mechanisms governing model and tool usage.
• Implement versioning, audit logging, and lineage tracking while maintaining documentation that supports full sy