Senior Python Backend Developer, ML Engineer

Intellectsoft

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

Posted 10/27/2026full-timeRemote • Brazil, Argentina, Poland, SpainSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and operationalizing ML and Deep Learning models, particularly with LLMs and Generative AI. Proficient in developing scalable backend systems using FastAPI and Python, while implementing MLOps practices for model management and optimization.

Highest-signal resume keywords

Python ProgrammingFastAPI DevelopmentMLOps ImplementationLLM IntegrationData Security

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Hard Skills

Machine LearningDeep LearningBackend API DevelopmentSQLDependency InjectionFeature EngineeringModel ServingNLPDocument ProcessingBig Data Systems

Soft Skills

Problem-SolvingTeam CollaborationAdaptability

Tools & Technologies

Azure OpenAIGPT-4MLflowHugging FaceDockerKubernetesSQLAlchemyCeleryOpenAPI/SwaggerAzure DevOps

Certifications & Qualifications

Bachelor’s Degree in Computer ScienceMaster’s Degree in Computer Science

Industry Keywords

MLOpsGenerative AILLMData SecurityRegulated Industries

Tech Stack

Tools & technologies

AssemblyAWSAzureCloudDockerETLGoogle Cloud PlatformKubernetesMicroservicesPythonSQLVault

About the role

Key responsibilities & impact

Build, refine, and use ML Engineering platforms and components; develop scalable backend systems, APIs, and microservices using FastAPI.
Implement MLOps including model KPI measurement, tracking, model drift detection, and model feedback loops.
Deploy and operationalize ML and Deep Learning models, focusing on LLMs and Generative AI.
Integrate Azure OpenAI, GPT-4, GPT-4 Vision, and other LLM providers with retry logic and error handling.
Maintain knowledge of state-of-the-art LLM, GenAI, and transformer technologies.
Scale machine learning algorithms for massive datasets under strict SLAs.
Build and orchestrate feature engineering, inferencing, and continuous model training pipelines.
Write production backend code in Python and SQL using object-oriented and asynchronous programming.
Implement dependency injection patterns and layered architecture.
Build LLM observability 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 LLM cost optimization strategies.
Integrate third-party APIs and services, including document/OCR services, cloud storage, and enterprise systems.
Collaborate with client-facing teams on business context and technical requirements.
Write testable, maintainable production code handling edge cases and errors.
Follow architecture and coding guidelines and participate in design/code reviews.
Write unit and higher-level tests.
Troubleshoot using structured logging and distributed tracing.
Use bug tracking, code review, version control, and related delivery tools.
Participate in scrum calls and agile ceremonies.
Document application changes and API documentation via OpenAPI/Swagger.
Research emerging architecture patterns and technologies through proofs-of-concept and prototypes.

Requirements

What you’ll need

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 LangChain and LangGraph 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 LlamaIndex 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.
Understanding of DevOps, CI / CD including Docker containerization, Azure DevOps pipelines or GitHub Actions, Kubernetes (nice to have).
Data security including multi-tenant data isolation, secure key management (Azure Key Vault), and audit trail implementation.
Experience designing on cloud platforms including Azure, AWS, or GCP.
Experience in data engineering in Big Data systems including large-scale data processing and ETL/ELT pipelines.
Rate limiting and quota management for high-throughput API usage.
Cost management and optimization for LLM usage at scale.
Document processing expertise, including PDF extraction and OCR tooling.
Production incident management and on-call experience.
Testing strategies for non-deterministic LLM outputs.
Domain knowledge in regulated industries is a plus.

Benefits

Comp & perks

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
Flexible hours & work setup - work from anywhere and organize your day your way