Senior AI Engineer

Role Description

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.

What You’ll Build

• 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.

What You’ll Own (Responsibilities)

• 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
  • PostgreSQL + 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

Qualifications

• 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:
  • LangChain / LangGraph
  • LlamaIndex
  • AutoGen / CrewAI-style multi-agent patterns
• Strong understanding of retrieval and vector search concepts: embeddings, indexing, filtering, evaluation.

Preferred

• Experience with vector databases and/or search stacks (e.g., Pinecone, Chroma, Weaviate, Qdrant, pgvector).
• Experience designing evaluation systems (offline eval, human eval loops, production monitoring, prompt/model regression).
• Experience building voice/real-time systems (streaming, WebRTC or similar), and/or integrating speech (STT/TTS) into production applications.
• Experience building durable, long-running workflows (Temporal or similar orchestration engines).
• Familiarity with observability tooling (OpenTelemetry, Datadog, or similar).
• Experience shipping multi-tenant SaaS systems with strong privacy boundaries.

Interview Focus Areas

• System design for agentic applications (state, memory, evaluation, failure modes).
• Practical retrieval/RAG design (data modeling, indexing, relevance, latency).
• Production engineering practices (testing strategy, observability, rollouts).
• Ability to communicate tradeoffs and make good technical decisions under uncertainty.

Compensation & Logistics

• Compensation: Competitive salary commensurate with experience (Senior level)
• Location: Remote
• Type: Full-time
• Requirements: Overlap with Americas timezones for collaboration; reliable high-speed internet