LLM Integration Engineer

Budget: 1300 EUR Deadline: 5 days

Hello,

I have hands-on experience building backend systems that connect structured data to LLMs for natural language querying, with a strong focus on reliability, guardrails, and production readiness.

Your task is very clear: this is not just “chat with data,” but a controlled NL → structured query workflow where accuracy, fallback logic, and maintainability matter. That is the right approach for compensation benchmarking data, especially with financial services clients.

I can help with:

building the backend service and SQL data layer outside Bubble

implementing safe retrieval from structured private data

exposing clean REST endpoints for Bubble

formatting benchmark outputs for client-facing use

adding fallback logic for low-confidence / missing data cases

keeping the system stable, debuggable, and production-ready

I’ve worked on LLM-assisted backend workflows, API integrations, structured data handling, and production logic where hallucinations are not acceptable. I’m comfortable owning the delivery from architecture through implementation and iteration, and I can communicate progress clearly in plain English.

40 hours/week at 35 EUR/hour works for me.

If useful, I can also share how I would structure the retrieval layer and guardrails before development starts.

Best regards
Проєкт 1
LLM-асистент для роботи з аналітичними даними.

Система дозволяла ставити запитання природною мовою до структурованої бази даних (PostgreSQL).
Я реалізував backend-сервіс на Python (FastAPI), який:

приймав запит користувача

передавав його в LLM

перетворював NL → SQL

виконував запит до БД

повертав структурований результат

Вивід формувався у вигляді таблиці або короткого звіту.
Для запобігання помилок використовувалася перевірка SQL-запитів перед виконанням.

Проєкт 2
Система пошуку по внутрішній документації.

LLM використовувався разом з RAG-підходом:
дані індексувалися у векторну базу (FAISS), після чого модель отримувала тільки релевантні фрагменти.

Я реалізував:

ingestion pipeline

retrieval logic

API для фронтенду

обробку запитів

2. Найбільш схожий проект (NL → база даних → звіт)

Я працював над системою, де користувач міг задавати запитання до структурованих даних.

Джерело даних: PostgreSQL
Підхід: NL → SQL

Потік роботи:

користувач вводить запит природною мовою

LLM генерує SQL-запит

система перевіряє його (schema validation)

виконує запит

формує структурований звіт

Вивід:

таблиця

коротке текстове пояснення

Якщо даних не вистачало, систе Expand

Budget: 200 EUR Deadline: 1 day

✋ Hello! We are dZENcode, a software development company.

We will deliver a backend system connecting your structured compensation data to an LLM via API, with reliable SQL data extraction, REST endpoints compatible with Bubble, AI-driven transaction parsing, and PDF/structured output, relying on experience, best practices, and in-house tooling.

Are there predefined mappings of compensation categories?
Which LLM models or endpoints are preferred for integration: GPT-4, Claude, or others?

For reference: Freelancehunt
Final cost is confirmed after we align the scope and acceptance criteria.

Let’s keep all communication and payments on this platform until the contract is in place.

___________________
Best regards,
dZENcode

Our strengths:
• 10+ years in software delivery (outsourcing & outstaffing)
• 90+ in-house specialists
• From-scratch builds and long-term support
• SLA + post-release maintenance
• Accountable delivery with a legal entity
• 250+ public reviews since 2015 Expand

Budget: 25 EUR Deadline: 3 days

Hello! The task is completely clear to me. For the financial compensation platform, the main challenge is not just connecting the LLM, but minimizing hallucinations and ensuring that every query to your database is mathematically accurate and secure. I have confirmed experience in developing RAG systems and NL-to-SQL agents that work with structured data, and I am ready to develop a backend layer that perfectly integrates with your Bubble frontend. My approach to implementing your project: 1. NL → Structured Query Architecture: • Instead of direct SQL generation by LLM models, I will implement Intermediate Representation (IR) or strict schema validation. The model first generates a logical query that goes through a validator before becoming SQL code. This eliminates incorrect selections. • Using LangGraph or LlamaIndex to create agents that understand the specifics of compensation (currencies, regions, grades). 2. Backend and API (Python/FastAPI): • I will create a custom backend on FastAPI, deployed independently from Bubble. • I will open secure REST endpoints that Bubble will call through the API Connector. • I will implement a clear output data structure: JSON for displaying graphs/tables on the screen and an integrated service (e.g., ReportLab or WeasyPrint) for generating PDF reports. 3. Accuracy Control and Hallucination Guardrails: • Implementation of "Data Privacy & Thresholds" logic: if the selection for the query is below the statistical threshold (for example,

Budget: 1200 EUR Deadline: 1 day

Hello!
I have experience building backend systems and integrating LLMs (OpenAI / Claude) with structured data sources. I work mainly with Python, FastAPI and PostgreSQL and have implemented retrieval pipelines where natural language queries are converted into structured queries and processed through an API layer.

For similar tasks I typically design a backend service that:
connects structured datasets to an LLM via API
implements a reliable retrieval layer exposes clean REST endpoints for frontend integration
includes guardrails and fallback logic to avoid hallucinations when data is missing

I focus on building production-ready services with stable performance, clear error handling and maintainable architecture.
Would be happy to discuss the dataset structure and the expected query workflow to propose the best technical approach.
I also provide post-delivery support and guarantee the quality of my work. Expand

Budget: 1500 EUR Deadline: 7 days

Good day

My name is Dmytro, from King Kong Lab. We work with AI integrations, backend services, and systems where LLMs connect to structured databases for processing natural language queries. Your project fully aligns with our experience.
We have developed solutions where LLMs operate over private data through the NL → SQL and RAG approach. The backend analyzes the user's query, transforms it into a structured database query, and then returns a verified result in an understandable format. In such systems, we have used PostgreSQL, Python/FastAPI, LangChain, or LlamaIndex to manage query logic and integrate with GPT models.
In one of the similar projects, the system accepted natural language queries, transformed them into SQL queries to a structured database, and returned analytical reports. We implemented several levels of data verification: the LLM generates the query, but execution is controlled by backend logic, which checks the SQL structure, access to tables, and the correctness of the result. This helps avoid hallucinations and ensures that the response is based only on real data.
We also have experience creating backend APIs for integration with various frontend systems. We typically build REST APIs (FastAPI) that provide an endpoint for natural language queries and an endpoint for obtaining structured results. Such architecture easily connects to Bubble or another frontend via API. For security, we use token-based authorization, request logging, data access control, and usage auditing.
For projects with private data, we restrict LLM access only to prepared queries or a retrieval layer. Data is not directly passed to the model — it receives only the structure and context. Additionally, we implement fallback logic: if data is missing or insufficient, the system returns a clearly marked result instead of generating assumptions.
We can implement a backend service for you that:
creates an NL → SQL layer for natural language queries
connects to your private compensation database
returns structured results for the Bubble interface
ensures stability, logging, and production-ready architecture
generates client reports or PDFs if needed
We are ready to work independently, regularly report on progress, and explain technical solutions in an understandable manner.
We can start working soon. I will send my resume and examples of relevant projects in private messages. Expand

Budget: 1000 EUR Deadline: 3 days

Good day!

I specialize in production-ready LLM integration with structured data, specifically NL→SQL and RAG patterns. I build robust, Bubble-compatible backend APIs, delivering accurate, reliable, and independently-owned solutions. My experience ensures your compensation data platform is precise.

Let's connect to discuss this further.

Budget: 35 EUR Deadline: 1 day

Hello, I have experience with LLM, implemented several solutions for various domains, for data augmentation and aggregation from different sources (structured and unstructured) with a black box approach and HITL. Let's discuss the details and create a complete technical specification before starting development. For any additional questions, please send a private message. We look forward to collaborating.

Budget: 1100 EUR Deadline: 7 days

Hello, Andreys! I am ready to start working immediately, I will implement the main backend layer on the n8n platform, write to me to discuss the details and deadlines.

Budget: 25 EUR Deadline: 1 day

I am among the top 10 developers in the category of "Artificial Intelligence and Machine Learning" among ~2100 specialists on the platform.
I guarantee:
- Fast and high-quality task execution
- Strict adherence to deadlines
- Regular communication throughout the entire process
I would be happy to discuss the details of your project in private messages.

Budget: 280 EUR Deadline: 1 day

Уважаемый клиент, благодарю за возможность подать заявку на разработку production-ready backend сервиса для natural language запросов к private compensation database. Ниже мое предложение. 🚀

https://flowcv.com/resume/i1q62s3oeo

📌 Scope of Work
✅ LLM + Structured Data Integration
Разработка backend-сервиса, который подключает вашу структурированную compensation database к LLM API для обработки natural language запросов.

✅ Reliable Retrieval Layer
Реализация безопасного подхода NL → structured query / SQL, чтобы модель не “угадывала”, а извлекала данные только из проверенной базы.

✅ Custom Backend + API for Bubble
Создание отдельного backend и SQL data layer вне Bubble с REST API для:
• отправки запроса
• получения форматированного benchmarking output
• передачи статусов ошибок / fallback ответов.

✅ Guardrails & Fallback Logic
Логика “insufficient data”, proxy ranges с явной маркировкой, защита от hallucinations и прозрачная обработка отсутствующих данных.

✅ Production Readiness
Стабильность, логирование, clear error handling, maintainable code, понятная архитектура для дальнейшего развития.

✅ Optional PDF Output
При необходимости могу добавить генерацию client-ready PDF reports.

🚀 My Solution & Ability
🔹 опыт разработки LLM apps поверх structured databases
🔹 практический опыт NL-to-SQL / RAG / retrieval pipelines
🔹 разработка REST API backend для внешних front-end систем
🔹 strong focus на accuracy, auditability и anti-hallucination design
🔹 умею объяснять технические решения простым английским языком для non-technical stakeholders

⚡ Technology Stack
Backend: Python / FastAPI or Node.js
LLM layer: OpenAI / Claude / hybrid routing
Database: PostgreSQL / SQL
Orchestration: LangChain / custom pipeline
PDF: HTML-to-PDF / templated reporting

🔚 Conclusion
Я могу взять проект под ключ: от проектирования retrieval-логики и backend API до production-ready сервиса, который Bubble frontend сможет использовать безопасно и стабильно.

Буду рад обсудить структуру данных, expected query types и формат benchmarking outputs. Expand

Budget: 1400 EUR Deadline: 5 days

Dear Hiring Manager,

I’m excited to apply for this role. I have hands-on experience building production-ready LLM applications that connect structured databases to natural language interfaces using NL → SQL workflows, RAG patterns, and tools like LangChain and LlamaIndex.

I can build a reliable backend service that translates user queries into accurate database queries, exposes clean REST APIs for your Bubble frontend, and returns structured benchmarking outputs. I focus heavily on guardrails, fallback handling (e.g., “insufficient data”), and stable performance to ensure the system is accurate and production-ready—especially important for financial services use cases.

I’m comfortable owning the full development process and communicating progress clearly to non-technical stakeholders. I’d be happy to share examples of similar systems I’ve built.

Best regards,
Jeo Vincent Carretas