Senior AI Engineer; Core - Supernal

Senior AI Engineer About Supernal

Supernal helps small-to-medium businesses hire their first AI employee. Our AI teammates are built using intelligent, agentic workflows deployed on a proprietary platform. We deliver working, value-generating AI Employees—not tools—that handle real business processes alongside human teams.

We’re hiring a Senior AI Engineer to build and ship the first generation of personalized, self‑improving agentic workflows that users rely on daily. This is an “end-to-end” role: you’ll design the agent runtime, memory + retrieval systems, evaluation harnesses, and the product-facing surfaces that put agents in front of real users at scale.

You should be equally comfortable reasoning about distributed systems and data (latency, caching, queues, failure modes, cost) as you are with modern agent stacks (tool use, memory, RAG, multi-step planning, guardrails, and evaluation).

This role will partner closely with platform engineering to leverage and extend our core services (Django backend, event-driven systems, Kubernetes, observability) while owning critical parts of the AI application layer.

• Personalized agent runtime: Agentic workflows that adapt to a user’s preferences, data, and ongoing behavior over time.

• Memory & retrieval systems: Short/long-term memory, durable state, and retrieval pipelines across vector DBs and relational data.

• Voice experiences (real‑time + async): Speech‑to‑speech/voice agents, streaming audio pipelines, turn‑taking, interruption handling, latency tuning, and QA for natural conversations.

• Agent evaluation + reliability: Offline/online evals, regression suites, red‑teaming, monitoring, and rollout controls so agents are trustworthy in production.

• Production agent infrastructure: Scalable orchestration patterns for multi-step jobs, background tasks, and user-facing interactions (sync + async), with clear SLAs/SLOs.

• Tooling + developer experience: Libraries and primitives that make it easy for the team to build new agent capabilities quickly and safely.

• Ship user-facing agent experiences end-to-end: prototype → production → iteration based on real usage.

• Architect and implement stateful agent systems (workflows, tool calling, memory, retrieval, and human-in-the-loop where needed).

• Build voice features end-to-end where they unlock value: realtime speech agents, voice UI/UX, prompt/audio routing, and guardrails for safe tool execution.

• Build/own an evaluation harness
  :

  • curated test sets + scenario suites

  • automated scoring / rubric-based graders

  • prompt/model/version tracking

  • canary + A/B experimentation and safe rollout patterns

• Design data + retrieval pipelines:

  • chunking, enrichment, metadata strategy

  • hybrid retrieval (vector + keyword + structured filters)

  • re‑ranking, caching, and latency optimization

  • multi‑tenant safety and data isolation

• Integrate with and extend our platform primitives:

  • Django/DRF/ASGI services

  • async execution + queues + workflow orchestration

  • Postgre
    
    SQL + pgvector

  • Kubernetes deployments, autoscaling, and cost controls

• Establish engineering rigor for agents:

  • observability (traces, spans, structured logs)

  • reliability patterns (timeouts, retries, circuit breakers, graceful degradation)

  • security/privacy controls for data access and tool execution

• Strong software engineering fundamentals (design, testing, code quality, performance, security).

• Production experience deploying AI systems in front of users (not just notebooks/demos).

• Experience building agentic or LLM‑powered systems with memory and tool use
  .

• Comfort working across application + infrastructure layers: APIs, background jobs, data stores, and deployment.

• Hands‑on experience with at least one agent framework (or equivalent custom implementation), such as:

  • Lang Chain / Lang Graph

  • Llama Index

  • Auto Gen / CrewAI‑style multi‑agent patterns

• Strong understanding of retrieval and vector search concepts: embeddings, indexing, filtering, evaluation.

• Experience with vector databases and/or search stacks (e.g., Pinecone, Chroma, Weaviate, Qdrant, pgvector).

• Experience designing evaluation systems (offline eval, human eval loops,…