AI LLM Architecture Senior Analyst

About the Role
pAs a hands-on bAI Engineer /b, 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. /ppYou 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. /ppYou will build and assemble various AI agent system components, including: /pulliIndividual agent logic /liliTool integrations /liliSkills and capability components /liliMemory components /li /ulpYou will also contribute to the development and integration of foundation models and traditional machine learning models into end-to-end AI pipelines. /ppA practical curiosity for the open-source ecosystem is essential. You will continuously evaluate, learn, and adopt relevant libraries and frameworks covering areas such as: /pulliAgent orchestration /liliVector storage /liliModel serving /liliMachine Learning pipelines /li /ulpYou 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. /ppYou will design enterprise-grade components that meet requirements for: /pulliSecurity /liliObservability /liliGovernance /liliPerformance /liliScalability /li /ulpYou will create and maintain technical deliverables related to your engineering work, including: /pulliLow-level design documents /liliComponent specifications /liliIntegration contracts /li /ulpThis ensures that your developments are well documented, testable, and easily transferable. /ppYou 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. /ppThis 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. /ph3Key Responsibilities /h3AI Agents and Multi-Agent SystemsulliDesign, develop, and configure individual agents, including their prompts, tools, and skills, and integrate them into multi-agent workflows. /liliImplement agent orchestration logic to manage task handoffs, communication, and error recovery. /liliDevelop prompt assembly logic and manage the information provided to models within their context windows. /liliImplement memory components that store and retrieve conversational history and relevant context. /li /ulFoundation Models and Fine-TuningulliIntegrate foundation models into applications by selecting the most appropriate model and invocation strategy for each use case. /liliDesign and execute fine-tuning pipelines, including data preparation and model training, to adapt models for specific business domains. /liliApply a strong understanding of Transformer-based architectures. /li /ulRetrieval-Augmented Generation (RAG)ulliDevelop ingestion pipelines capable of parsing, chunking, enriching, and indexing unstructured enterprise content for search and retrieval purposes. /liliImplement embedding generation, integrate vector databases, and develop retrieval components, including connectors and adapters required to support end-to-end RAG (Retrieval-Augmented Generation) pipelines. /li /ulEvaluation and Quality AssuranceulliDesign evaluation frameworks and test suites to measure agent and component quality using metrics such as: Accuracy Relevance Faithfulness /liliShare findings and recommendations to drive continuous design improvements. /li /ulSecurity, Governance, and ComplianceulliImplement guardrails, content filtering mechanisms, and protections against prompt injection attacks. /liliDevelop PII (Personally Identifiable Information) detection and redaction components. /liliIntegrate access-control mechanisms governing model and tool usage. /liliImplement versioning, audit logging, and lineage tracking while maintaining documentation that supports full system auditability. /li /ulObservability and OperationsulliInstrument components with logging and tracing capabilities to monitor: Requests Responses Token consumption Tool invocations /liliContribute to monit