Advanced AI Engineer

We are seeking a seasoned professional to take on the role of Advanced AI Engineer. This is an exciting opportunity for someone who enjoys turning complex reasoning patterns into robust, high-quality services that balance safety, quality, and cost at scale.

This position involves designing and implementing production-grade components for an AI-first platform, leveraging knowledge-graph reasoning and strong retrieval capabilities. You will be responsible for building agent components with typed JSON I / O, integrating knowledge-graph reasoning, and implementing a high-quality baseline path for side-by-side comparisons.

Key Responsibilities :

• Agent Implementation : Design and harden Intent, Planner, and Router / Composer agents with retries / timeouts, idempotency, and call-graph traces.
• Knowledge-Graph Reasoning : Develop correct graph queries (SPARQL / Gremlin / PGQL) from planner outputs, perform subgraph extraction, and encode rationale and references in responses.
• RAG Baseline & Retrieval : Implement document prep, chunking / embeddings, hybrid retrieval and reranking, and maintain a high-quality baseline path.
• Prompt / Config Tuning : Version and tune prompts, routing policies, temperature / top-p settings, and caching, and document routing outcomes and cost / latency budgets.
• Evaluation Hooks : Integrate test sets and scoring faithfulness / correctness, precision / recall, multi-hop coverage, latency, and enable automated re-evaluation on any change model / agent / prompt / data.
• Observability & Cost Controls : Instrument traces / metrics / logs token usage, latency P50 / P95, error codes, surface cost-per-answer dashboards, and implement backpressure and graceful degradation.
• Security & Guardrails : Enforce policy-as-code and entitlement checks role / row / column, PII / PHI handling, content moderation, and HITL approval prompts for state-changing actions.

We are looking for an individual with hands-on experience in LLM engineering, including tool / function-calling, agent frameworks, and prompt / version management. Strong skills in Knowledge Graphs RDF / SPARQL or property graph / Gremlin and RAG pipelines chunking, embeddings, retrieval / reranking are also required. Proficiency in Python and / or TypeScript / Node.js, strong REST / gRPC API design, and experience with major model platforms are desired.

A successful candidate should have expertise in Observability & Reliability, including OpenTelemetry traces / metrics / logs, performance profiling, resiliency patterns circuit breakers, bulkheads, DLQ / queues, OIDC / SSO, secrets management, least-privilege access, audit logging, and secure coding for AI / data services. Additionally, experience with CI / CD & Testing, Git-based workflows, automated pipelines, unit / integration / contract tests, and environment promotion practices is necessary.

This role is ideal for a hands-on engineer who can turn advanced reasoning patterns into robust services, balancing quality, safety, and cost at enterprise scale.