(Senior) experience shipping ML systems into production (not just notebooks).
Strong programming skills in
Python
and familiarity with ML libraries (e.g.,
scikit-learn, PyTorch, TensorFlow, XGBoost ).
Experience with data processing and analytics using tools such as
Pandas, NumPy, SQL, Spark
(as applicable).
Solid understanding of ML fundamentals: bias/variance, evaluation metrics, cross-validation, feature engineering, and error analysis.
Experience building and deploying ML services (e.g.,
FastAPI/Flask ), containerization ( Docker ), and CI/CD.
Ability to communicate tradeoffs and results clearly to both technical and non-technical stakeholders.
Key Responsibilities
Model Development & Applied AI
Partner with stakeholders to frame business problems as
ML/AI use cases , define success metrics, and identify required data.
Build and iterate on models using appropriate approaches (e.g., classification, regression, ranking, clustering, anomaly detection, NLP).
Perform feature engineering, dataset creation, labeling strategies, and model evaluation with strong scientific rigor.
Implement techniques for
model interpretability , bias assessment, and responsible AI where applicable.
Production Engineering & MLOps
Build production-grade ML services and pipelines (batch real-time), ensuring
performance, reliability, and maintainability .
Deploy models to cloud environments using CI/CD and infrastructure-as-code best practices.
Implement monitoring for
data drift , model drift, latency, throughput, cost, and model quality.
Maintain versioning for datasets, features, models, and experiments to ensure repeatability and governance.
Data & Platform Collaboration
Collaborate with data engineering to create robust data pipelines and ensure data quality.
Work with software engineers to integrate ML into applications through APIs, event streams, or workflow orchestration.
Document architecture, operational runbooks, and model cards; participate in reviews and knowledge sharing.
Must Have
4 years of experience developing APIs and integrating with 3rd party APIs. Strong SQL skills (SQL Server or Oracle). Familiarity with scripting languages (Python, Bash, Powershell). Experience with version control systems (Git, GitHub, GitLab). Knowledge of CI/CD pipelines and DevOps best practices. Understanding of workflow automations is a plus. Excellent problem-solving, analytical, and communication skills
Additional Info
Is responsible for developing, implementing and maintaining knowledge-based or artificial intelligence application systems.
The individual should ensure that information is converted into a format that is digestible and easy for end users to access the information and utilize it optimally.
ESSENTIAL FUNCTIONS:
Designs and writes complex code in several languages relevant to our existing product stack, with a focus on automation.
Configures, tunes, maintains and installs applications systems and validates system functionality.
Monitors and fine tunes applications system to achieve optimum performance levels and works with hardware teams to resolve issues with hardware and software.
Develops and maintains department's knowledge database containing enterprise issues and possible resolutions.
Develops models of task problem domain for which a system will be designed or built.
Uses models, hypotheses, and cognitive analysis techniques to elicit real problem-solving knowledge from the experts.
Mediates between the expert and knowledge base; encodes for the knowledge base.
Acts as subject matter expert for difficult or complex application problems requiring interpretation of AI tools and principles.
Researches and prepares reports and studies on various aspects of knowledge acquisition, modeling, management, and presentation.
Develops and maintains processes, procedures, models, and templates for collecting and organizing knowledge into specialized knowledge representation programs.
Acts as vendor liaison for products and services to support development tools.
Maintains the definition, documentation, training, testing, and activation of Disaster Recovery/Business Continuity Planning to meet compliance standards.
Maintains a comprehensive operating system hardware and software configuration database/library of all supporting documentation to ensure data integrity.
Acts to improve the overall reliability of systems and to increase efficiency.
Works collaboratively with cross functional teams, using Agile / DevOps principles to bring products to life, achieve business objectives and serve customer needs.