Our customer's product is an AI-powered platform that helps businesses make better decisions and work more efficiently. It uses advanced analytics and machine learning to analyze large amounts of data and provide useful insights and predictions.

The platform is widely used in various industries, including healthcare, to optimize processes, improve customer experiences, and support innovation. It integrates easily with existing systems, making it easier for teams to make quick, data-driven decisions to deliver cutting-edge solutions.

Requirements

Bachelor's or Master's degree in Computer Science or a related field

Strong Python coding skills - 7+ years

2+ years of hands‑on experience with machine learning and production LLM systems

Experience building backend APIs with FastAPI, async patterns, rate limiting, and SQLAlchemy - 3+ years

Experience designing maintainable and extensible systems using dependency injection, interfaces, and abstract base classes

Experience with vector databases such as Pinecone, Weaviate, or Chroma, as well as hybrid search

Strong understanding of RAG architectures, including retrieval, reranking, context assembly, and response generation

Hands‑on experience with Lang Chain and Lang Graph for building and orchestrating LLM workflows

Advanced Python skills, including async/await, type hints, Pydantic, and SOLID principles

MLOps experience with MLflow, model versioning, and A/B testing; experience with Langfuse is a plus

Experience in NLP and computer vision, including document understanding, OCR, and GPT-4 Vision

Experience building feature pipelines, real‑time and batch inference systems, and model serving

Hands‑on experience with Hugging Face is required; experience with Llama Index is a plus

Familiarity with database technologies such as SQL

Good problem‑solving skills and the ability to work in a fast‑paced, team‑oriented environment

Nice to have skills

Understanding of Dev Ops, CI / CD including: Docker containerisation, Azure Dev Ops pipelines or Git Hub Actions, Kubernetes (nice to have)

Data security including:



Multi‑tenant data isolation, Secure key management (Azure Key Vault), Audit trail implementation

Experience in designing on cloud platform including: Azure (strongly preferred): Azure OpenAI, Blob Storage, Key Vault, Container Registry, AWS or GCP

Experience in data engineering in Big Data systems including: Large‑scale data processing, ETL/ELT pipelines

Rate limiting and quota management for high‑throughput API usage

Cost management and optimisation for LLM usage at scale

Document processing expertise (PDF extraction, OCR tooling)

Production incident management and on‑call experience

Testing strategies for non‑deterministic LLM outputs (e.g., golden datasets, fuzzy matching)

Domain knowledge in regulated industries (e.g., healthcare/pharma workflows, regulatory compliance) is a plus

Responsibilities

Build, refine, and use ML Engineering platforms and components; develop and implement scalable backend systems, APIs, and microservices using FastAPI

Implement MLOps including model KPI measurement, tracking, model drift detection, and model feedback loops

Deploy and operationalise ML and Deep Learning models, with a strong focus on LLMs and Generative AI

Integrate Azure OpenAI (GPT-4, GPT-4 Vision) and other LLM providers with proper retry logic and error handling

Maintain up‑to‑date knowledge of state‑of‑the‑art technologies such as LLMs, GenAI, and transformer architectures

Scale machine learning algorithms to work on massive data sets under strict SLAs

Build and orchestrate model pipelines including feature engineering, inference, and continuous model training





Write backend application code in Python and SQL using strong object‑oriented principles and asynchronous programming (asyncio, async/await)

Implement dependency injection patterns and layered architecture (Service, Foundation, Orchestration, DAL)

Build LLM observability (e.g., Langfuse) to track prompts, tokens, costs, and latency

Develop prompt management systems with versioning and fallback mechanisms

Implement Celery (or similar) workflows for asynchronous task processing and complex pipelines

Build multi‑tenant architectures with client data isolation

Implement cost optimisation strategies for LLM usage (prompt caching, batch processing, token optimisation)

Integrate third‑party APIs and services (e.g., document/OCR services, cloud storage, enterprise systems)

Collaborate with client‑facing teams to understand business context and contribute to technical requirement gathering

Write production‑ready code that is testable, maintainable, and accounts for edge cases and errors

Ensure high quality of deliverables by following architecture/design guidelines, coding best practices, and periodic design/code reviews

Write unit tests and higher‑level tests to handle expected edge cases and errors gracefully

Troubleshoot backend application code using structured logging and distributed tracing

Use bug tracking, code review, version control, and other tools to organise and deliver work

Participate in scrum calls and agile ceremonies, communicating progress, issues, and dependencies

Document application changes and updates, including API documentation via OpenAPI/Swagger

Research and evaluate emerging architecture patterns and technologies through rapid learning, proofs‑of‑concept, and prototypes

Benefits

Awesome projects with an impact

Udemy courses of your choice

Team‑buildings, events, marathons & charity activities to connect and recharge

Workshops, trainings, expert knowledge‑sharing that keep you growing

Clear career path

Absence days for work‑life balance

Adaptable hours & work setup - work from anywhere and organise your day your way

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📌 Senior Python Backend Developer / ML Engineer (Buenos Aires)
🏢 Intellectsoft
📍 Buenos Aires