Master Thesis focusing on developing machine learning models for lithium - ion cell sorting at Fraunhofer LBF. Involvement in innovative projects addressing circular economy in battery recycling.
About the role
- Software Engineer managing cloud infrastructure and ML model implementations for FactSet. Collaborating with engineers to support and deliver AI solutions.
Responsibilities
- Manage and deploy various cloud-based infrastructure
- Develop a roadmap for management and growth of existing pipelines and infrastructure for serving ML and AI solutions
- Deployment and maintenance of models, databases, and applications
- Support work on various AI/ML projects including entity and topic modeling, semantic tagging/enrichment, information extraction, transfer learning, and graph neural networks
- Develop dashboards and visualizations for financial experts
- Ingest and analyse structured and unstructured data
- Develop processes for data collection, quality assessment, and quality control
- Keep up to date with state-of-the-art approaches and technological advancement
- Collaborate with other Engineering teams
Requirements
- Bachelor’s or Master’s degree in Computer Science, Machine Learning or a related field
- 3 + years of working experience as a Software Engineer/ Machine Learning Engineer
- Experience with cloud-based infrastructure (AWS preferred)
- Familiarity with ML, NLP and GenAI (including RAG, Prompt Engineering, Vector DBs)
- Successful history of writing production grade code and releasing in an enterprise environment
- Team player
- Fluent in English; ability to communicate about complex subjects to non-technical stakeholders
- Highly proficient in Python
- Experience with OpenAI, Anthropic, and other large language models.
- Prior experience working with unstructured data (text content, JSON records)
- Working with Agile development practices in a production environment
- Experience with AWS environment [SageMaker, S3, Athena, Glue, ECS, EC2]
- Experience with Agentic workflows and MCP
- Experience working with large volumes of data in a stream or batch processing environment.
- Prior experience with Docker and API development.
- Usage of MongoDB
- Familiarity with deep learning libraries (Keras, PyTorch, Tensorflow)
- Familiarity with big data tool chain (e.g. Pyspark, Hive)
- Experience with information extraction, parsing and segmentation
- Knowledge of ontologies, taxonomy resolution and disambiguation.
- Experience in Unsupervised Learning techniques Density Estimation, Clustering and Topic Modelling.
- Graph database experience (AWS Neptune, Neo4j)
Benefits
- Flexible working hours
- Professional development opportunities
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
Tech skills
Location requirements
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