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.
Responsibilities
– Design, develop, and deploy machine learning models for predictive analytics, classifi cation, 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 defi ne 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.
Requirements
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 eff ective.
– Strong ability to debug, optimize, and improve models for performance and interpretability.
Collaboration & Communication
– Works well with cross-functional teams including data engineers, software developers, and product managers.
– Communicates technical concepts clearly to non-technical stakeholders.
– Documents and maintains ML workfl ows to ensure reproducibility and scalability.
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.
Mandatory Skills
– Machine Learning
– Python
– Docker
– AWS
– SQL
Mandatory Languages
– English
Working Day
– Full Time Job
Working Conditions
– Remote-friendly role with flexible working hours (EST timezone).
– Collaborative team environment with an emphasis on problem-solving and innovation.
– Opportunity to work on diverse ML problems and contribute to end-to-end ML pipelines.