Job Description

Expertise in Model Development:

Develop and fine-tune machine learning models. Ensure models are accurate, efficient, and tailored to our specific needs.

2. Quality Assurance:

Rigorously evaluate models to identify and rectify errors. Maintain the integrity of our data-driven decisions through high performance and reliability.

3. Efficiency and Scalability:

Streamline processes to reduce time-to-market. Scale AI initiatives and ML engineering skills effectively with dedicated model training and testing.

4. Production ML Monitoring & MLOps:

Implement and maintain

model monitoring pipelines

to detect data drift, concept drift, and model performance degradation. Set up alerting and logging systems using tools such as Evidently AI, WhyLabs/Prometheus + Grafana or cloud-native solutions (AWS SageMaker Monitor, GCP Vertex AI, Azure Monitor

)

. Collaborate with teams to integrate monitoring into CI/CD pipelines, using platforms like Kubeflow, MLflow, Airflow, and Neptune.ai. Define and manage automated retraining triggers and model versioning strategies. Ensure observability and traceability across the ML lifecycle in production environments.

Qualifications

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Qualifications:

5+ years of experience in the respective field. Proven experience in developing and fine-tuning machine learning models. Strong background in quality assurance and model testing. Ability to streamline processes and scale AI initiatives. Innovative mindset with a keen understanding of industry trends. License/Certification/Registration

Job Location

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