Integrate CV, NLP, and LLM capabilities into applications, implementing input validation, preprocessing, inference, and structured output handling.
Build data and integration pipelines connecting images, documents, text, databases, model services, and enterprise applications.
Work with Principal Scientists to implement agreed model interfaces, evaluation workflows, and production readiness requirements.
Develop automated unit, integration, API, and regression tests, including checks against representative data and expected model behavior.
Make deployments reproducible and support safe updates through versioned model artifacts and configuration, CI/CD, and appropriate release controls.
Improve reliability and performance through logging, metrics, profiling, and optimization of latency, throughput, memory use, and processing cost.
Investigate production incidents and unexpected outputs, distinguishing application, integration, data, and model-related issues with the relevant specialists.
Contribute to technical design, secure development practices, documentation, code reviews, and mentoring within agreed architectural standards and delivery priorities.
Your Qualifications, Your Influence
5+ years of professional software engineering experience, with substantial hands-on experience developing and supporting Python backend applications.
Strong Python fundamentals, including object-oriented design, asynchronous programming, data structures, and maintainable software design.
Experience designing REST APIs and using a Python backend framework such as FastAPI, Flask, or Django.
Strong SQL skills and experience with relational databases, data transformation, validation, and integration workflows.
Practical experience integrating ML models into production applications, with experience in at least one relevant area such as computer vision, NLP, or LLM applications.
Understanding of inference workflows, preprocessing and postprocessing, and how changes to data, models, or configuration can affect application behavior.
Experience writing automated tests with tools such as pytest, diagnosing production issues, and improving application performance.
Working experience with Git, Docker, Linux, CI/CD, and deploying applications in a cloud environment.
Understanding of API security, authentication and authorization, logging, monitoring, and reliable error handling.
Ability to work independently, explain technical trade-offs, and communicate clearly in English across locations and disciplines.
Preferred Skills and Technical Familiarity
Experience in one or more of the following areas would be valuable
Computer vision, OCR, document or image-processing pipelines, and tools such as OpenCV.
NLP and LLM applications, including information extraction, text classification, embeddings, and Hugging Face Transformers.
ML frameworks and inference tooling such as PyTorch, TensorFlow, or ONNX.
Semantic search, vector databases, RAG, or knowledge graph integration.
Model serving, MLOps, evaluation automation, model versioning, and production model monitoring.
Integration with enterprise platforms, including .NET-based applications, or experience in supply chain, procurement, and electronic component distribution.
Success in the First 90 Days
Understand your assigned services, business workflows, data and model dependencies, deployment processes, and production risks; document important gaps.
Deliver a meaningful feature or production fix through the full development lifecycle, supported by appropriate tests and documentation.
Implement an agreed improvement to service reliability, inference performance, observability, or processing quality, using evaluation criteria established with the relevant engineering and scientific stakeholders.
EQUAL OPPORTUNITY EMPLOYER.
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