Senior ML Engineer

A Senior ML Engineer is part of a key team of Mercans Technology professionals that applies scientific, mathematical, and social principles to design, build, and maintain technology products, systems, and solutions in the Data Science and Machine Learning domain. These DS/ML technology products and solutions provide exceptional customer experiences while meeting the growing needs of the business.

This hybrid role bridges the gap between machine learning engineering and data science, focusing on designing, developing, and deploying scalable data-driven solutions using cloud technologies.

This position is focused on managing design, development, implementation, and support activities of DS/ML components of the Mercans global payroll software HR Blizz (HRB), and other Mercans solutions, and their integration with third-party Human Capital Management (HCM), Workforce Management (WFM), Enterprise Resource Planning (ERP), banking and other ancillary software solutions.

Duties and Responsibilities

ML Engineering

Design, build, and maintain scalable ML pipelines for data preprocessing, training, and deployment.

Optimize machine learning models for performance, reliability, and scalability in cloud environments (AWS or GCP).

Develop and deploy APIs or services for real-time and batch inference.

Implement CI/CD workflows for ML models and monitor their performance in production.

Data Science

Analyze large datasets to extract actionable insights and inform strategic decisions.

Develop predictive and prescriptive models using statistical and machine learning techniques.

Perform A/B testing, experimentation, and hypothesis validation for product features.

Create visualizations and dashboards to effectively communicate findings to stakeholders.

Collaboration

Work closely with the Data engineers, ML Ops engineer, and Senior Data Scientist to ensure efficient data workflows and model integrations.

Partner with product teams to understand business requirements and translate them into technical solutions.

Provide mentorship and guidance to junior team members.

Education and Experience

Education & Experience

Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related field.

4+ years of experience in machine learning engineering or data science roles.

Technical Skills

Proficiency in Python for data analysis and model development.

Solid understanding of machine learning frameworks (e.g., TensorFlow, PyTorch, or Scikit-learn).

Experience with data processing tools like Spark or Pandas.

Strong knowledge of SQL and familiarity with NoSQL databases.

Hands-on experience deploying ML models in production using AWS (e.g., SageMaker, Lambda, S3, EC2) or GCP (e.g., Vertex AI, Cloud Functions, BigQuery, Cloud Storage).

Hands on experience with developing RAG applications

Cloud Expertise

Proficient in cloud-native services for model training, deployment, and monitoring.

Knowledge of containerization (Docker) and orchestration tools like Kubernetes.

Soft Skills

Strong analytical and problem-solving skills.

Excellent communication skills, with the ability to explain technical concepts to non-technical stakeholders.

Nice to Have

Experience with MLOps tools (e.g., MLflow, TFX, or Kubeflow) for managing the machine learning lifecycle.

Familiarity with streaming data platforms (e.g., Kafka, AWS Kinesis, GCP Pub/Sub).

Knowledge of reinforcement learning, NLP, or computer vision.

Exposure to data governance, security, and compliance best practices in cloud environments.

Certification in AWS (e.g., AWS Certified Machine Learning – Specialty) or GCP (e.g., Google Professional Data Engineer).

Understanding of DevOps principles and experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation).