About this opportunity
PLP Group lists this Senior Python Data Engineer / Applied AI Engineer opportunity in northern, Kentucky. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Senior Python Data Engineer / Applied AI EngineerJob detailsProductUnited StatesFull-timeJob Description**Senior Python Data Engineer / Applied AI Engineer**---**About The Role*** Join a cybersecurity company building AI-enabled data products.* Work on data integrations, normalization, data quality, and applied AI features.* Help turn complex security and IT data into reliable, useful product intelligence.* Own technical problems end-to-end, from investigation through production-ready implementation.**What You’ll Do*** Build and improve Python-based data processing pipelines.* Work with structured and semi-structured data, especially JSON from third-party APIs.* Improve data quality, consistency, deduplication, and traceability across integrations.* Investigate third-party API documentation and identify better ways to collect and use available data.* Design and implement new integration logic for security, identity, cloud, SaaS, endpoint, and infrastructure data sources.* Write clear, maintainable Python code for production data workflows.* Create tests and validation checks to catch schema changes, missing data, malformed records, and edge cases.* Work with SQL and PostgreSQL to analyze, debug, and improve data flows.* Contribute to internal tooling that helps the team build, review, and maintain integrations faster.* Support applied AI/ML features related to classification, enrichment, ranking, entity matching, summarization, or data analysis.* Collaborate closely with product and engineering to turn messy real-world data into reliable product capabilities.**What We’re Looking For****Required Qualifications*** A Bachelor’s degree in Engineering is required, though a Master’s degree is preferred.* 5 + years of relevant professional experience in data engineering.* Strong software engineering fundamentals: clean code, testing, debugging, version control, and maintainable production systems.* Engineering, quantitative, or technical background with strong professional software development experience.* Strong experience working with APIs, JSON, data transformation, and backend data pipelines.* Strong Python experience, with broader programming experience in other languages welcome.* Solid SQL skills, ideally with PostgreSQL.* Experience with ETL/ELT workflows and production data processing.* Ability to reason through inconsistent third-party data and design robust normalization logic.* Comfortable reading external technical documentation and translating it into working code.* Strong debugging and problem-solving skills.* Good testing habits using tools such as pytest.* Ability to work independently and own complex technical work with minimal supervision.* Clear communication when documenting assumptions, tradeoffs, and implementation decisions.**Relevant Tools & Technologies*** Python* PostgreSQL / SQL* dbt or similar data transformation tooling* REST APIs* JSON schema validation or data-quality frameworks* pytest* Git* Cloud data services, especially Azure or similar platforms* Observability/logging tools**Applied AI / ML Experience*** Practical experience using LLM APIs for extraction, classification, enrichment, or internal tooling.* Practical experience with classical machine learning techniques for classification, regression, clustering, ranking, anomaly detection, or entity matching.* Experience with embeddings, semantic search, retrieval, or ranking systems.* Familiarity with libraries such as scikit-learn, sentence-transformers, FAISS, BM25, LightGBM, PyTorch, or similar.* Ability to use AI where it adds leverage while keeping core data logic reliable, testable, and explainable.**Nice To Have*** Cybersecurity, IT, cloud, identity, or infrastructure data experience.* Experience with security tools, compliance data, vulnerability data, endpoint data, or SaaS administration data.* Experience with data contracts, schema evolution, lineage, or data observability.* Experience building internal developer or data-review tools.* Familiarity with security/compliance concepts such as risk scoring, controls, frameworks, or remediation workflows.
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