Software & AI Engineer

Software & AI Engineer - JD Company Name: Alethic Insights

Website: Alethic Insights Executive Intelligence Systems LinkedIn: Alethic Insights LinkedIn

If you love owning full-stack products end-to-end, thrive on building production-grade Python services and polished React interfaces, and get genuinely excited about LLMs and agents actually doing things — this role was written for you.

At our end, we are building our flagship platform that blends data, workflow automation, and agentic AI to help enterprises make smarter decisions, faster. We are looking for a hands-on engineer with 3–6 years of relevant experience who can own services end-to-end: design, build, deploy, monitor, and iterate. You’ll ship production code, shape the architecture, and help us push the boundaries of what agentic AI can do inside a real product.

Why This Role Is Different

• Real ownership. You own modules end-to-end — design, ship, monitor, iterate.

• True full-stack ownership. React frontends, Python APIs, cloud infra, AI pipelines, auth, observability — the whole stack, your call.

• AI that’s more than hype. Build and own RAG pipelines, vector search, LLM integrations, and autonomous agents that do actual work inside a real enterprise product.

• Small team, big leverage. Direct collaboration with senior engineers and the founding team. No layers, no politics.

• Career trajectory. A clear path to senior engineering and tech-lead roles as NUDGE scales.

What You’ll Actually Do

• Own features end-to-end across the full stack. Take a feature from design doc to production — React UI, FastAPI service, database schema, AWS deployment, and monitoring — without handing off to another team. You are the team.

• Build and ship React interfaces that enterprise users actually love. Design and implement component hierarchies, manage state across complex workflows, integrate with REST APIs, handle async data gracefully, and make sure the UI stays responsive even when an AI agent is doing heavy work in the background.

• Design and ship Python/FastAPI services that are built to last. Clean router structure, Pydantic-validated contracts, dependency-injected auth, background task queues (Celery/RQ), async I/O where it matters — and the discipline to write tests before you call something done.

• Implement and own end-to-end RAG pipelines. Not just “plug in LangChain and call it done” — you’ll choose the right chunking strategy for the document type, select and fine-tune embedding models, stand up and query vector stores (Pinecone, pgvector, or Weaviate), add reranking, and instrument retrieval quality metrics so you know when the pipeline degrades in production.

• Build agentic AI workflows that actually complete tasks. Design multi-step agent graphs using LangGraph or similar, wire up tool-calling against internal APIs and external services, handle failure modes and retry logic, and evaluate agent performance — not just “did it finish” but “did it finish correctly.”

• Own the data layer from schema to query plan. Model relational data in PostgreSQL with multi-tenancy and row-level security in mind, write queries a DBA would be proud of, reach for Redis or DynamoDB when the access pattern demands it, and know how to spot an N+1 before it becomes a production incident.

• Deploy and operate your own services on AWS. Containerize with Docker, orchestrate on ECS or Lambda, store secrets in Secrets Manager, wire IAM with least privilege, set up RDS with proper connection pooling, and build CloudWatch dashboards and alarms so you know when something is wrong before a user files a ticket.

• Implement auth you’d stake your reputation on. JWT issuance and validation, OAuth 2.0 / OIDC flows, role-based access control, secure session management, and the habit of asking “can this endpoint be called by the wrong tenant?” before every PR merge.

• Ship with a CI/CD pipeline, not a prayer. Set up GitHub Actions (or equivalent) pipelines that lint, test, build, and deploy — so merging to main means it’s in production, not “it’s waiting for someone to SSH into a box.”

Must-Have Skills

• Generative AI & RAG: you’ve built a RAG pipeline in production — not just followed a tutorial. You understand the tradeoffs between chunking strategies (fixed-size, semantic, document-structure-aware), have selected and compared embedding models, have stood up at least one vector store (Pinecone, pgvector, W…