Full-Time Machine Learning Engineer (FinServ)
Sigma Software is hiring a remote Full-Time Machine Learning Engineer (FinServ). The career level for this job opening is Experienced and is accepting Tiranë, Albania based applicants remotely. Read complete job description before applying.
Sigma Software
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Assist in building and testing Generative AI demos and POCs.
Support the design of simple, scalable architectures for Generative AI applications.
Work with team members to integrate AI components into larger systems.
Use MLOps practices to help automate parts of the model development process.
Follow guidance to ensure Generative AI applications are secure and meet basic governance standards.
Help deploy AI applications on cloud platforms or on-premises setups with team support.
Adapt to a fast-paced environment with evolving project requirements.
Keep up with AI trends and apply them to projects with guidance.
Advise clients. Understand their needs, analyze possible solutions, and present the best options.
4+ years of experience in IT with at least 2-3 years of experience in machine learning.
Solid Back-end engineering skills, particularly with Python (e.g., Django, Flask, or FastAPI).
Experience in pre-sales and opportunity processing.
Basic experience with databases or tools like vector databases (e.g., Pinecone, Weaviate, Faiss).
Familiarity with AI frameworks such as TensorFlow, PyTorch, or Hugging Face.
Understanding of CI/CD pipelines.
Knowledge of RAG or AI application basics (security, governance, etc.).
Experience with cloud platforms (AWS, Google Cloud, Azure) or on-premises setups.
Strong problem-solving skills and ability to handle shifting priorities with Support team.
Experience with client-facing roles.
Excellent presentation and demonstration skills.
Bachelor's or Master's degree in computer science, machine learning, artificial intelligence, or a related field.
Upper-Intermediate level of English.
WOULD BE A PLUS:
- Contributions to open-source projects or experience with tools like Airflow or Spark.
- Familiarity with containers (e.g., Docker) or orchestration tools (e.g., Kubernetes).
- Experience in the Banking and Financial Services domain.
- Exposure to prompt engineering or fine-tuning LLMs.
- Knowledge of other languages like Java or Go.