Senior Software Engineer, AI-Native Enterprise Platform

About InterOpera

InterOpera is pioneering intelligence infrastructure that eliminates key enterprise pain points — driving cost efficiency, profitability, and scalable growth. Through real-time decision-making capabilities, we empower clients to identify challenges, generate actionable insights, implement solutions, and track outcomes. Our solutions span sales optimization, risk management, and energy efficiency. We build at the intersection of applied AI and enterprise SaaS — and we are scaling fast across Asia.

The Role

As a Senior Software Engineer, you are a senior individual contributor who ships the critical path. You will architect and build agentic systems and RAG pipelines end-to-end, make pragmatic technical trade-offs, and raise the quality bar through the code you write and the reviews you give. This is a hands-on role first. Leadership is optional — but valued. The engineers we are most excited about lead from the front: they mentor teammates, drive design reviews, and set technical direction for a small group while staying deep in the code. Formal people-management experience is not required — grow into more team-leadership scope if you want it, or remain a high-impact builder; both are valued. Indicatively ~80% deep technical work / ~20% technical influence, with the leadership portion scaling to your appetite

Responsibilities

AI-Native Product Engineering — the core of the role

• Production agentic systems: architect and ship multi-step, tool-using AI agents with planning and memory that automate enterprise workflows in sales, risk, and energy.
• RAG pipelines: design and operate retrieval over proprietary client data — chunking strategy, embedding models, vector store selection (pgvector, Pinecone, Weaviate, Qdrant), hybrid search, and re-ranking.
• sLLM deployment: evaluate and deploy small/specialized LLMs (Llama, Mistral, Qwen, Gemma) with attention to fine-tuning (LoRA/QLoRA), quantization, inference optimization, and self-hosting for cost, latency, and data-residency.
• Evaluation harnesses: measure faithfulness, hallucination rate, latency, and instruction-following across model and prompt versions — so AI quality is engineered, not hoped for.
• Frontier APIs: integrate Anthropic Claude, OpenAI, and Google where appropriate, and help define a clear frontier-vs-self-hosted strategy.

Hands-On Development

• Backend: build and maintain high-performance services in Python, with Go or Java for performance-critical paths.
• Frontend: build modern, responsive interfaces in React.js / Next.js where the work calls for it, ensuring intuitive enterprise UX.
• Cloud & ops: drive deployment on AWS / GCP / Azure with strong CI/CD, IaC, observability, and on-call discipline.

Technical Influence & Mentorship — optional, valued

• Lead technical design reviews; write clear RFCs and architecture docs.
• Mentor mid-level engineers through code review, pairing, and coaching — leading by example.
• Partner with Product and Business stakeholders; translate ambiguous problems into clear technical execution.
• (If you choose to) take point on a small feature pod and set its technical direction.

Requirements

Must-Have Experience

• 6+ years of professional software engineering, shipping enterprise-grade software at scale (multi-tenant, secure, observable, performant).
• Hands-on, production experience building AI agent systems — multi-step reasoning, tool use, planning, and memory — using LangGraph, LangChain, LlamaIndex, CrewAI, or equivalent in-house orchestration. This is a hard requirement: we want engineers who have shipped real agentic systems to production — not prototypes or course projects.
• Strong Python, plus Go or Java for performance-critical paths; comfortable owning services from design through 24/7 production operation.
• Production RAG experience: vector databases, embedding pipelines, hybrid search, query planning, and metadata filtering.
• Solid frontend ability with React.js / Next.js (or equivalent) — enough to own a feature end-to-end.
• Strong fundamentals in distributed-systems design.

Strongly Preferred

• sLLM / open-source LLMs — fine-tuning (LoRA, QLoRA, SFT/DPO), quantization, inference optimization, self-hosting.
• LLM evaluation — building evals, faithfulness/hallucination measurement, regression testing for prompt and model changes.

Leadership — Optional

• Experience mentoring engineers, running design reviews, or informally leading a small team is a plus — but formal management / tech-lead experience is not required. What matters is that you can lead hands-on when it counts.

Mindset & Culture Fit

• Results-oriented: you make technical decisions with clear awareness of business impact and commercial trade-offs.
• Initiative-driven self-starter; adaptable to a fast-paced startup with shifting priorities; growth mindset.
• Excellent English communication (written and verbal); able to translate complex technical ideas for diverse, multicultural audiences. Bahasa Indonesia is a plus

Nice to Have (Bonus)

• Knowledge graph / GraphRAG experience — building entity/relationship graphs from unstructured data, graph databases (Neo4j, Memgraph), and graph-aware retrieval. Given where our platform is heading, this is a meaningful bonus.
• Experience in one of our domains: enterprise sales automation, risk management / RegTech, or energy / sustainability tech.
• MLOps / LLMOps: model serving, AI observability, prompt versioning, A/B testing of model behavior.
• Data platform engineering: streaming, feature stores, lakehouses (Snowflake, Databricks, BigQuery).
• Open-source contributions, technical writing, or speaking at industry events.

Work Environment

• High-intensity startup: fast-paced, demanding workload — we ship fast and iterate faster. Occasional weekend work when launches require it.
• Flexible hours & hybrid: flexibility expected for project deadlines and global team collaboration across time zones.
• Indonesia-based with regional collaboration across Asia.

What We Offer

• Competitive salary commensurate with experience, plus performance bonus.
• Meaningful equity — own a real piece of what you build.
• Direct ownership of the AI-Native platform powering enterprise decisions across the region.
• A small, senior, ambitious team where your work is visible and your impact is measurable.
• Learning budget for conferences, courses, and books — we invest in keeping you sharp.
• Modern tech stack and the freedom to make the right architectural calls.