Software Engineer

Qualifications

• 3+ years building backend systems (or equivalent impact in a fast-paced environment).
• Strong in one or more modern languages for services/data work
• Proven experience designing and operating distributed systems and data pipelines (streaming and batch).
• Solid grasp of data modeling, storage, indexing, and query performance (SQL and NoSQL).
• Track record shipping secure, reliable APIs (auth, rate limits, versioning).
• Comfortable owning cloud infrastructure, containers, CI/CD, and observability end to end.
• Performance-minded: profiling, tuning, and cost-aware architecture.
• Security-first mindset: authN/Z, secrets, least-privilege, and data stewardship.
• Bias to action, clear communication, and comfort with ambiguity/zero-to-one builds.
• AI integrations: hands-on with LLM providers and tooling (OpenAI/Anthropic, vector stores, embeddings, RAG, function/tool calling, streaming).
• Agentic systems: experience orchestrating multi-step/agent workflows (queues, graph/executor patterns, retries, tool sandboxes, safety/guardrails).
• Prompt/data engineering: versioned prompts, evaluation harnesses, offline/online A/B for prompts/tools, dataset curation, and feedback loops for continuous improvement.

Responsibilities

• Own core backend architecture and services from design to production.
• Build data ingestion, normalization, and orchestration across multiple sources.
• Design and ship internal/external APIs that are reliable, scalable, and well-documented.
• Define storage and indexing strategies to power fast, accurate retrieval.
• Establish SLIs/SLOs, instrumentation, and on-call practices for high availability.
• Set up CI/CD, testing, and developer tooling for rapid, safe iteration.
• Champion security, privacy, and resilience across the stack.
• Collaborate tightly with product/design to scope thin slices and ship quickly.
• Raise the engineering bar via code reviews, standards, and mentorship.
• Contribute to early hiring and help shape Seefy’s engineering culture.
• Own AI plumbing: provider abstraction layers, model routing/policies, cost/latency controls, caching, and fallbacks.
• Build agent infrastructure: tool registries, permissioning, execution graphs, checkpoints, and human-in-the-loop escalation.
• Operationalize prompts & evals: prompt/version management, golden datasets, regression tests, eval dashboards, and production feedback ingestion for continuous learning.

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