AI Engineer (Middle/Senior)

About us

We’re a software outsourcing company that ships AI-powered products for EdTech and Healthcare clients. A big slice of our work is chat/voice conversational engines over knowledge bases and operational processes; we also build recommendation features, data pipelines, and full-stack integrations. We are cloud-agnostic and work across AWS and Azure (experience in either is a plus). Our delivery culture: lean Agile, CI/CD, code reviews, pragmatic documentation.
 

What you’ll do

• Design & build LLM applications: RAG over client KBs, multi-tool/agent graphs, prompt strategies, and voice UX (STT/TTS).
• Implement retrieval & data flows: ingestion, chunking, metadata/embeddings, hybrid retrieval, re-ranking, caching.
• Own evaluation & quality: latency/cost budgets, A/Bs.
• Automate with n8n (or similar): connect services, data, and back-office workflows; expose reliable admin ops for non-engineers.
• Ship pipelines: Airflow for orchestration; dbt for transformations/semantic layer where relevant.
• Productionize: tracing/observability, feature flags, safe rollouts, secrets & config hygiene.
• Collaborate with clients: discover scope, explain trade-offs, demo progress; optional presales spikes/estimates.

Must-have qualifications

• Strong Python (typing, tests, packaging) and web/service basics (FastAPI, Docker).
• Hands-on building LLM apps with one or more: LangChain, LangGraph, AutoGen, Dify (or equivalents).
• RAG end-to-end experience: corpus prep → embeddings → retrieval → re-rank → context assembly → measurable gains.
• Evaluation mindset: design offline eval sets and release gates; reason about faithfulness vs relevancy; control p95 latency & $/session.
• Cloud proficiency in AWS or Azure (using at least one in production).
• Comfort with observability (OpenTelemetry/LangSmith/Prometheus/Grafana or similar).
• English B2+; clear written & verbal client communication.

Nice to have

• Vector DBs: pgvector, Qdrant, Pinecone, Azure Cognitive Search (vector); rerankers (BGE/monoT5/Cohere/ColBERT).
• Voice: Azure Speech, Whisper; VAD/barge-in, turn-taking logic.
• Data stack: dbt, Airflow, event streams; data contracts.
• Workflow: n8n design for non-tech ops; governance of low-code automations.
• Frontend for AI UX (React/Next.js, TypeScript).
• Compliance awareness for EdTech (FERPA/COPPA) and Healthcare (HIPAA/GDPR).

Leveling expectations

Mid-level

• Implements features from clear designs; contributes to prompts, tools, retrieval tweaks.
• Writes reliable code and tests; adds tracing; participates in eval set curation.
• Operates one cloud (AWS or Azure); comfortable with n8n recipes and simple Airflow DAGs.

Senior

• Leads solution design E2E; chooses retrieval/agent patterns; defines latency/cost targets.
• Establishes eval harness & release gates; drives A/Bs and post-release monitoring.
• Shapes client scope & trade-offs; mentors others; improves platform reusable components.
• Comfortable across both clouds or deep in one; designs robust n8n/Airflow/dbt topologies.