Building the Next Generation of Club Industry Leaders
Software Engineer (Full stack- AI)
Location
India
Posted
73 days ago
Salary
0
Seniority
Mid Level
No structured requirement data.
Job Description
Software Engineer (Full stack- AI)
Clubessential
Role Description This role is about engineers who build the AI features our users actually interact with — chat interfaces, agents, retrieval systems, AI-powered workflows inside our product. We're hiring a full stack engineer who can ship LLM-powered features end-to-end: from the prompt and the eval suite, through the retrieval and orchestration layer, to the UI a user clicks. You should be the kind of person who has gone deep on LLMs, has strong opinions about evals, and rolls their eyes at demos that don't survive contact with real users. If your strengths are general full stack engineering rather than LLM-specific work, see also our Full Stack Engineer role — it may be a closer fit. If "AI feature" still means "ChatGPT wrapper" to you, this isn't your team. If you've already shipped something into production and watched it break in interesting ways, keep reading. You'll own AI-powered features end-to-end — model choice, prompts, retrieval, tool use, the orchestration around it, the UI on top, and the evals that keep it from regressing. The work spans research-flavoured experimentation and hard product engineering, often in the same week. Concretely, in your first six months you'd expect to: - Ship at least one significant AI feature into production — agent, RAG flow, generative UI, or similar - Build out our eval and observability stack so we can ship LLM changes with confidence rather than vibes - Drive a measurable improvement on a key model-quality metric (accuracy, latency, cost, hallucination rate) - Help shape our internal AI engineering practices — model selection, prompt versioning, regression testing Day-to-day, you'll write code (a lot of it AI-assisted), design and evaluate prompts, debug agent failures, partner with product and design on what AI features should even be, and make calls on trade-offs between cost, latency, and quality. Qualifications - A genuine willingness to learn. - Adaptability across stacks. - Strong fundamentals. - System design at both altitudes. - Sharp problem-solving. - Hands-on experience building AI features in production. - Evals as a first-class skill. - Production realism about LLM features. - Strong full stack engineering chops. - Comfortable with both database paradigms. - Production cloud experience. - Fluency with AI-assisted development. - High autonomy. Requirements - Real features that real users hit, with real consequences when they fail. - Experience with several of: - LLM APIs (Anthropic, OpenAI, or open-weight models via vLLM/Bedrock/Together) and have opinions on which to use when - Retrieval-augmented generation: chunking strategies, embeddings, vector stores (pgvector, Pinecone, Weaviate, Qdrant), hybrid search - Agentic systems: tool use, multi-step workflows, planning, MCP, frameworks like LangGraph/Inngest/Temporal or your own orchestration - Structured output, function calling, and JSON-mode reliability - Streaming responses and the UX patterns that make them feel good - Prompt engineering as a serious discipline — versioning, A/B testing, regression suites, not vibes - Production realism about LLM features. - Strong full stack engineering chops. - Comfortable with both database paradigms. - Production cloud experience. - Fluency with AI-assisted development. - High autonomy. Nice to have - Fine-tuning or post-training experience (LoRA, RLHF, DPO) — even small-scale - Built or contributed to open-source AI infra, agent frameworks, or eval tooling - Experience with model routing, prompt caching, or other cost/latency optimizations at scale - Worked on AI safety, red-teaming, prompt injection defence, or content moderation pipelines - Background in IR, NLP, or applied ML before the LLM era - Strong writing — this team values it, and AI features live or die by precise language - Prior experience in a small, fast-moving team (under ~30 engineers) Got questions? You can email us at talentsupport@xplortechnologies.com
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