Machine Learning Engineer

Position Summary

We are seeking a Machine Learning Engineer with a strong background in building and
deploying ML models, programming in Python, and working with cloud-based infrastructure.
The ideal candidate should be capable of designing end-to-end machine learning pipelines,
containerizing applications using Docker, and deploying solutions in a cloud environment. They
should also have the ability to break down complex business problems into ML tasks and
assess whether machine learning is the right solution.
This role is well-suited for individuals who are hands-on, analytical, and pragmatic in applying
ML solutions to real-world challenges.

Key Responsibilities
⚪    Design, develop, and deploy machine learning models for predictive analytics,
classification, NLP, and other data-driven tasks.
⚪    Implement data pipelines for ingestion, preprocessing, feature engineering, and model
training.
⚪    Containerize ML models and applications using Docker for scalable and reproducible
deployments.
⚪    Deploy and maintain ML solutions in cloud environments (AWS/GCP).
⚪    Optimize model performance, latency, and resource utilization for real-time or batch
inference.
⚪    Monitor and troubleshoot ML models in production, ensuring reliability and robustness.
⚪    Collaborate with data engineers, software developers, and business stakeholders to
define project requirements and integrate ML models into production systems.
⚪    Conduct rigorous model evaluation using appropriate metrics to ensure performance
and fairness.
⚪    Assess whether machine learning is necessary for a given problem or if alternative
rule-based/statistical approaches are more appropriate.

Required Qualifications & Skills
Technical Skills
⚪    Machine Learning & AI: Strong understanding of ML techniques (supervised &
unsupervised learning), NLP, deep learning basics, and model evaluation.
⚪    Programming: Proficiency in Python, including frameworks such as TensorFlow,
PyTorch, Scikit-Learn, Pandas, and NumPy.
⚪    Docker & Containers: Experience in containerizing ML applications using Docker for
deployment.
⚪    Cloud Platforms: Experience with at least one cloud provider (AWS, GCP)
⚪    Data Handling & Pipelines: Experience working with large datasets, SQL/NoSQL
databases, and ETL pipelines

Problem-Solving & Analytical Thinking
⚪    Ability to break down complex problems into well-structured ML tasks.
⚪    Can determine if ML is necessary or if a simpler solution (e.g., heuristic rules, statistical
methods) would be more effective.
⚪    Strong ability to debug, optimize, and improve models for performance and
interpretability

Nice-to-Have Skills
⚪    Understanding of business impact of ML models and how to align them with
organizational goals.
⚪    Experience with feature stores, model registries, and ML model lifecycle management.