Job Responsibilities and Duties
- Build AI-assisted solutions for code analysis, system transformation, refactoring, and migration.
- Analyze application architecture, source code, dependencies, data, metadata, and logs.
- Design and integrate generative AI, NLP, retrieval, and other machine learning capabilities into migration workflows.
- Develop reliable pipelines to generate, test, validate, and deploy transformed code and software components.
- Create evaluation methods for functional correctness, regression, performance, security, and maintainability.
- Build cloud-based AI services, APIs, data pipelines, and automation for production use.
- Apply sound software engineering practices, including clean code, testing, documentation, versioning, and traceability.
- Provide technical guidance and mentor engineers while remaining directly involved in design and development.
Qualifications
- 5–10 years of relevant experience in software engineering, AI, machine learning, or system modernization.
- MSc or PhD in Computer Science, Machine Learning, Software Engineering, or a related field.
- Strong Python programming skills and experience delivering production software.
- Experience with generative AI, NLP, embeddings, retrieval-augmented generation, or related AI methods.
- Experience with PyTorch, TensorFlow, or equivalent machine learning frameworks.
- Strong understanding of software architecture, APIs, dependency management, testing, and system integration.
- Experience working with structured and unstructured data, SQL, and data transformation pipelines.
- Experience with cloud platforms such as Azure, AWS, or Google Cloud, and container technologies such as Docker.
- Ability to explain technical decisions clearly and work effectively in evolving problem spaces.
Additional Qualifications
- Experience with code intelligence, static analysis, parsers, abstract syntax trees, compilers, or dependency graphs.
- Experience migrating or modernizing applications across languages, frameworks, databases, or cloud environments.
- Experience with MLOps tools, model evaluation, experiment tracking, monitoring, and scalable inference.
- Experience building developer tools or AI-assisted software engineering platforms.
- Experience with enterprise systems in Energy, Insurance, Healthcare, or another regulated industry.
