Firsdt Derivative is looking for a highly skilled Senior Machine Learning Engineer to lead the design, development, and deployment of machine learning models and systems that solve complex problems. The successful candidate will have a strong background in machine learning, software engineering, and data analysis, as well as excellent communication and collaboration skills. The Senior Machine Learning Engineer will work closely with cross-functional teams to identify opportunities for AI-driven innovation, develop prototypes, and deploy scalable solutions that meet business needs.
Essential Requirements:
- Strong programming skills in Python use for both ML and automation tasks and experience with libraries like TensorFlow, PyTorch, scikit-learn, Keras, H2O, etc.
- Expertise in developing and deploying ML models in production environments using MLOps practices and tools.
- Experience in defining the processes, standards, frameworks, prototypes and toolsets in support of AI and ML development, monitoring, testing and operationalization.
- Experience with ML Ops tools and best practices with at least one of the most popular framworks (e.g., Kubeflow, MLflow, Datarobot,…).
- Experience with Big Data technologies such as Hadoop, Spark, or Kafka.
- Proficiency in SQL and experience with NoSQL databases.
- Knowledge of OpenShift / Kubernetes and hands on experience building CI/CD pipelines orchestration by Jenkins, GitLab CI, GitHub Actions or similar tools is a must-have.
- Knowledge of data pipelines orchestration through tools such as Airflow, Control-M
- Knowledge of Logging and Monitoring tools, such as Splunk and Geneos.
- Ability to bridge the gap between business, technology, and data architectures.
- Advanced knowledge of software design and development methodologies, including Agile and Waterfall.
Preferred Qualifications (Nice to Have):
- Experience in the financial industry.
- Familiarity with additional data processing libraries and tools.
- Experience with cloud platforms (e.g., AWS, GCP, Azure)
- Proven expertise in data modelling.
Soft Skills:
- Excellent problem-solving skills and attention to detail.
- Strong communication skills and the ability to work collaboratively in a team environment and ability to explain complex concepts to non-technical audiences
