Senior Software Development Engineer

You'll architect and ship across the full Genesis stack: agentic pipelines, backend APIs, data infrastructure, and clinical-facing UI. You'll work directly with founders and customers. You'll own things end-to-end. This is not a role where you bolt AI onto existing CRUD. You'll be making foundational decisions about how intelligent systems are designed, evaluated, and operated at scale in a regulated industry.
The core requirements for the job include the following:
You build robust backend systems:
6 + years building production web applications from scratch.
Deep Python proficiency; comfortable with FastAPI, Django, or Flask in production.
Experience designing APIs that serve both humans and AI agents (tool schemas, structured outputs, streaming).
Async-first thinking: asyncio, task queues, event-driven architectures.
Good to have Kafka, Redis, or ActiveMQ for real-time data movement.
Nice to have Postgres, Elasticsearch, MongoDB, or graph databases (Neo4j, TigerGraph) in production.
AI-native engineering is your default mode:
You've built production systems where LLMs are doing real work - not demos, not PoCs.
You've designed and shipped RAG pipelines, multi-agent workflows, or tool-using agents in production.
You understand prompt engineering as an engineering discipline: versioning, evaluation, and regression testing.
You've instrumented AI systems for observability - latency, token usage, hallucination rate, and drift.
You can reason about model tradeoffs (context length, cost, latency, accuracy) and make architectural calls accordingly.
You've worked with LLM SDKs (OpenAI, Anthropic, Bedrock, etc. ) and agentic orchestration frameworks (LangChain, LlamaIndex, CrewAI, or similar).
You operate at cloud scale:
Docker and Kubernetes in production - this is a hard requirement.
At least one public cloud (AWS, Azure, GCP) with real operational experience.
Microservices and cloud-native design patterns.
You've been on-call. You know what a bad deploy feels like at 2 am.
You can ship a frontend when the product demands it.
Nice to have React, TypeScript, or modern JS frameworks.
Enough frontend fluency to build clinical interfaces without a dedicated frontend handoff.
Bonus Points:
Led a small engineering team - mentored, reviewed, unblocked.
CKAD or equivalent Kubernetes certification.
ML/DL model deployment experience (PyTorch, scikit-learn).
Built evaluation harnesses or used MLflow, LangSmith, or similar for AI observability.
Healthcare domain experience (FHIR, HL7 clinical workflows).

Experience
6-8 yrs