Intellectsoft

Senior Python Backend Developer / ML Engineer (IR-555)

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SeniorZdalnieFull-time

#466695·Dodano 9 dni temu·3

Źródło: Intellectsoft

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Tech Stack / Keywords

Python FastAPISQLAlchemyLangChain LangGraphHugging FaceMLflowAzure OpenAIKubernetes SQL

Podsumowanie oferty

Develop scalable backend systems and ML components for an AI-powered analytics platform that provides business insights and predictions. The work includes Python APIs and microservices, machine learning and LLM workflows, and model operations.

Wymagania

Bachelor’s or Master’s degree in Computer Science or a related field.
7+ years of strong Python coding experience.
2+ years of hands-on experience with machine learning and production LLM systems.
3+ years of experience building backend APIs with FastAPI, asynchronous patterns, rate limiting, and SQLAlchemy.
Experience designing maintainable and extensible systems using dependency injection, interfaces, and abstract base classes.
Experience with vector databases such as Pinecone, Weaviate, or Chroma, and with 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 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.
Familiarity with database technologies such as SQL.
Problem-solving skills and ability to work in a fast-paced, team-oriented environment.

Nice to have:

Understanding of DevOps and CI/CD, including Docker containerization, Azure DevOps pipelines or GitHub Actions; Kubernetes is also noted.
Data security experience, including multi-tenant data isolation, secure key management (Azure Key Vault), and audit trail implementation.
Experience designing on cloud platforms, particularly Azure; Azure OpenAI, Blob Storage, Key Vault, Container Registry, AWS, or GCP.
Data engineering experience 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, such as golden datasets and fuzzy matching.
Domain knowledge in regulated industries, such as healthcare or pharma workflows and regulatory compliance.

Obowiązki

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 LLMs, GenAI, and transformer architectures.
Scale machine learning algorithms to work on massive datasets under strict SLAs.
Build and orchestrate model pipelines for feature engineering, inference, and continuous model training.
Write backend application code in Python and SQL, using object-oriented principles and asynchronous programming (asyncio, async/await).
Implement dependency injection patterns and layered architecture (Service, Foundation, Orchestration, DAL).
Build LLM observability, such as 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 optimization strategies for LLM usage, including prompt caching, batch processing, and token optimization.
Integrate third-party APIs and services, such as document/OCR services, cloud storage, and enterprise systems.
Collaborate with client-facing teams to understand business context and contribute to technical requirement gathering.
Write production-ready, testable, maintainable code that accounts for edge cases and errors.
Follow architecture and design guidelines, coding best practices, and periodic design and code reviews.
Write unit tests and higher-level tests to handle expected edge cases and errors.
Troubleshoot backend application code using structured logging and distributed tracing.
Use bug tracking, code review, version control, and other tools to organize 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 architecture patterns and technologies through rapid learning, proofs of concept, and prototypes.

Benefity

Udemy courses of your choice.
Team-building activities, events, marathons, and charity activities.
Workshops, trainings, and expert knowledge-sharing.
Absence days for work-life balance.
Flexible hours and work setup; work from anywhere and organize your day your way.

Szkolenia wewnętrzne

Spotkania integracyjne

Elastyczne godziny

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