Senior Python Systems Engineer (Performance & Scalability)

Key responsibilitiesAs a core member of our backend team, you will build scalable, high-performance API architecture and data pipelines that power enterprise data-driven solutions.

• Design, build and maintain RESTful APIs and microservices in Python using FastAPI, Django/DRF or Flask.
• Build asynchronous and distributed processing with Celery, RabbitMQ and Redis for high-volume metadata ingestion and enrichment jobs.
• Design schemas, indexes and queries across PostgreSQL, MongoDB and Elasticsearch, and tune them for large datasets.
• Integrate external APIs with retries, rate limiting and caching.
• Build LLM-enabled workflows: RAG pipelines, embeddings, vector search and structured information extraction from documents.
• Apply NLP techniques (NER, entity linking, text classification) to turn unstructured content into structured data.
• Secure services with JWT/OAuth2 authentication and role-based authorization.
• Containerise services with Docker and ship them through CI/CD (GitHub Actions, GitLab CI, Jenkins or Azure DevOps) to AWS or Azure.
• Own production health: structured logging, monitoring, alerting, debugging and root-cause analysis.
• Contribute to system design and architecture decisions, review code and mentor junior engineers.Required qualifications
• B.E./B.Tech in Computer Science or a related field, or equivalent experience.
• 5+ years building production backend systems in Python, with strong OOP, data structures and algorithms.
• Hands-on experience with at least two of FastAPI, Django/DRF and Flask.
• Solid SQL and schema design on PostgreSQL, plus working experience with MongoDB.
• Production use of Redis and a message broker (RabbitMQ or similar) with Celery or equivalent task queues.
• Experience designing secure REST APIs (JWT, OAuth2) and integrating third-party APIs.
• Comfortable with Docker, Git and at least one CI/CD tool; deployed services on AWS or Azure.
• A measurable track record of performance work, e.g. reducing API latency or raising throughput.
• Clear written and spoken communication; able to work with QA, DevOps and product teams in Agile sprints.Nice to have
• Elasticsearch or OpenSearch: mappings, aggregations and relevance tuning.
• Built RAG or agentic workflows with LLM APIs, vector databases and prompt engineering in production.
• NLP libraries such as spaCy or Stanza for NER and information extraction.
• Document processing: PDF/XML parsing, web scraping with BeautifulSoup or Selenium.
• Familiarity with scholarly publishing metadata (DOIs, JATS XML, Crossref, Unpaywall, ORCID, ROR).
• Node.js experience, to work across our mixed Python/Node services.
• Code-quality tooling such as SonarQube, and test-driven development with pytest.What we offer
• Work on platforms used by major publishers, funders and research institutions worldwide.
• Real ownership of services, from design through production.
• Hands-on exposure to LLM and knowledge-graph projects over scholarly content.
• Competitive salary, health insurance and a hybrid working model.