This is a hands-on role for a builder who codes daily, partners tightly with Product/Design/Platform, and uses AI tools (generation, retrieval, agents, automation) to move faster with higher quality .

What You’ll Do

• Ship product features end-to-end across backend, APIs, and UI (Python/TypeScript/React or equivalent).
• Use AI as a force multiplier: speed up specs, scaffolding, tests, refactors, data exploration, incident triage, and docs with modern AI tools.
• Add AI-powered user value when it helps: search & RAG, summarization/tagging, recommendations, assistants/automation — always measured against baselines.
• Instrument everything: define KPIs (quality, latency, cost, engagement), set up offline/online evals, A/B tests, and telemetry.
• Build reliable services with CI/CD, canaries/rollbacks, observability (metrics/logs/traces), and clear SLOs.
• Own data flows for features: ETL for embeddings/indices, feedback loops from clicks/dwell/ratings, guardrails for safety and privacy.
• Collaborate with Product/Design on discovery, UX, and roadmap; writes clear specs that feed into how the system is built, tested and documented (context engineering) and drive good decisions quickly.
• Ensure security/IAM and governance (PII handling, RBAC, auditability) in everything you ship.

You May Work With

• LLM providers and OSS (e.g., OpenAI/Anthropic + HF/Transformers), vector search, hybrid retrieval (BM25 + embeddings), lightweight orchestration (LangChain/LlamaIndex or in-house patterns).
• Docker/Kubernetes/Terraform, SQL + one NoSQL/graph store, feature flags & experimentation.

Requirements

• 7–10+ years in product/software engineering with a record of shipping user-facing features at scale.
• Strong Python; comfortable across API + web app stacks (FastAPI/Flask/Node, React).
• Demonstrated pragmatic use of AI in products and workflow: prompt/adapter tuning, retrieval, evals, and cost/latency trade-offs.
• Solid engineering fundamentals: system design, testing, code quality, performance, and operational ownership in cloud environments.
• Experience with instrumentation, A/B testing, and analytics to guide iteration.
• Security mindset (privacy, RBAC/IAM, auditability).

Nice to Have

• Knowledge graphs or personalization/recommendation experience.
• Agentic workflows (tool use, routing) with safety guardrails.
• ML/LLM observability, eval frameworks, or GPU inference optimization basics.
• Contributions to open-source or internal frameworks that improve developer velocity.

How We’ll Measure Success (First 90 Days)

• Ship at least one meaningful product feature with baselines for quality/latency/cost and a follow-up improvement plan.
• Stand up repeatable evals (offline + online) for an AI-powered capability (e.g., RAG or summarization).
• Demonstrate velocity gains from AI-assisted workflows (e.g., improved lead time/change failure rate) and document the playbook for the team.

Why Nscale

• Build user-facing product on a modern GenAI platform with real enterprise impact.
• Autonomy, trust, and a high bar — with competitive compensation and equity.
• Collaborate with experts across infra, systems, and applied research; learn fast and ship faster.

At Nscale, we are committed to fostering an inclusive, diverse, and equitable workplace. We believe that a variety of perspectives enriches our work environment, and we encourage applications from candidates of all backgrounds, experiences, and abilities. We strongly encourage applications from people of colour, the LGBTQ+ community, people with disabilities, neurodivergent people, parents, carers, and people from lower socio-economic backgrounds.

If there’s anything we can do to accommodate your specific situation, please let us know.

The responsibilities outlined in this job description are not exhaustive and are intended to provide a general overview of the position. The employee may be required to perform additional duties, tasks, and responsibilities as assigned by management, consistent with the skills and qualifications required for the role.

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