Senior AI Engineer

Role Overview

- Lead development of our AI-native productivity platform spanning a FastAPI service layer, Telegram bot interface, and CrewAI-powered autonomous agents.

- Own end-to-end Python delivery: async APIs, Celery pipelines, data models, and integrations with LLM tooling, Redis, and PostgreSQL.

- Champion reliability (pytest, coverage, mypy, ruff) and observability (Loguru JSON logs, Sentry, Prometheus) in a Docker- first workflow.

What You’ll Build

- Async FastAPI endpoints and background orchestrations that drive founder and team-focused automations.

- Celery workers/beat schedules coordinating reminders, task dispatchers, and memory compression jobs.

- SQLAlchemy models, migrations, and data access around PostgreSQL + Redis.

- Memory/agent services using LangGraph, LangChain, Mem0, and Qdrant vector search.

- Telegram bot flows via python-telegram-bot, integrating with our AI agents and Redis conversation store.

Must-Have Skills

- 6+ years professional Python with deep async expertise (FastAPI/Starlette, asyncio patterns, dependency injection).

- Production Celery experience: queue design, scheduling, observability across Redis backends.

- Strong SQLAlchemy/Alembic skills; comfortable tuning PostgreSQL schemas and async session lifecycle.

- Building and hardening API ecosystems (AuthN/Z with python-jose/passlib, rate limiting via SlowAPI).

- Hands-on with Redis (caching, broker, pub/sub) and message-driven architectures.

- AI/LLM integration experience—LangChain, CrewAI, OpenAI/Anthropic/Gemini clients, or custom agent workflows.

- Docker/Docker Compose fluency; capable of running multi-service stacks locally and in CI/CD.

- Testing discipline: pytest (including pytest-asyncio), fixtures,mocking, coverage, property-based or contract tests.

- Code quality guardrails: Black, Ruff, mypy, pre-commit pipelines.

- Observability mindset with structured logging (Loguru), Sentry tracing, LangSmith, and Grafana metrics.

Nice to Have

- Production experience with LangGraph, LangChain or similar multi-agent orchestration frameworks.

- Vector databases (Qdrant, Pinecone) and retrieval-augmented memory systems (Mem0, LLMLingua compression).

- Telegram Bot API or other conversational UX tooling.

- Background in task automation for knowledge workers or workflow SaaS products.

How We Work

- Python 3.13 codebase, type hinted, enforcing conventional commits and PR hygiene.

- Docker Compose stack (Postgres, Redis, Qdrant, Flower, Celery worker/beat, API, Bot).

- Structured environments via .env, secrets management, and rotating keys.

- Documentation/tests housed in docs/ and tests/, with ≥80% coverage expectations.