AI LLM Senior Engineer

• Independently design build and deliver software components across the AI architecture owning them end to end from design through implementation integration and testing as a hands-on practitioner

• Design and build AI agent architectures including individual agents their prompts tools and skills multi-agent orchestration and memory systems making deliberate design pattern and technology choices

• Design and implement agent orchestration patterns that handle task handoffs communication state management and error recoveryvalidatingthem through hands-on prototyping

• Evaluate multiple design options and technical approaches making deliberate justified design choices that balance capability cost efficiency performance and enterprise-grade reliability

• Design build and run evaluation strategies and harnesses that measure agent and system quality on metrics such as accuracy relevance and faithfulness translating findings into design improvements

• Architect and implement foundation model integrations selecting the right models invocation patterns and customization approaches (fine-tuning RAG custom integration) based on capability cost and performance trade-offs

• Design and build model adaptation and fine-tuning pipelines applying working knowledge of transformer-based architectures to inform model selection and optimization

• Design and build the AI context layer including context graph design and ingestion pipelines that parse chunk enrich and index structured and unstructured enterprise content and the retrieval components that ground AI outputs in the clients knowledge

• Build embedding vector storage and retrieval (semantic hybrid reranking) into end-to-end RAG pipelines applying integration patterns that connect to enterprise data sources

• Design and implement context assembly and memory components that manage prompts context windows and conversational state for groundedaccurateoutputs

• Identify design and build reusable components and solution patterns that accelerate delivery and can be templated across engagements

• Design for cost efficiency and performance optimizingmodel usage inference patterns caching and resourceutilizationto meet target latency throughput and cost objectives

• Design build andvalidatesystems against enterprise non-functional requirements implementing guardrails prompt-injection defenses PII handling and access controls for security and Responsible AI

• Build governance controls including versioning audit logging and lineage tracking and produce the model documentation that keeps systems auditable

• Build observability into systems logging tracing monitoring alerting and cost tracking to ensure AI solutionsremainhealthy performant and scalable in production

• Produce detailed architecture artifacts including architecture decision records (ADRs) architecture blueprints design documents agent orchestration and integration pattern specificationscomponentand data flow diagrams that guide and enable broader engineering teams

• Continuously learn evaluate and applynew designpatterns frameworks and technologies across the fast-evolving AI landscape balancing innovation with enterprise-grade reliability

• Collaborate with cross-functional delivery teams data engineers ML engineers and application developers to translate requirements into concrete architecture decisions that meet stakeholder needs

EDUCATION

• Bachelors Degree or equivalent

BASIC (REQUIRED) QUALIFICATION

• Minimum of 3 years of experience in designing & deploying AI / ML solutions using at least one cloud vendor as an AI/ML architect.

• Minimum of 1 year of experience in the AgenticLLMand Generative AI space.

• Minimum of 1 year of experience architecting and operationalizing LLM driven application architecture patterns.

• Minimum of 3 years in coding and engineering machine learning deep learning and NLP solutions and applications.Minimum of 2 years of coding experience using python