Machine Learning Engineer

Machine Learning Engineer role at Avahi

We're looking for an ML Engineer to join our delivery team and serve as a critical bridge between Data Science, Data Engineering, and cloud practices. In this role, you'll help translate experimental models into production-ready systems, strengthen our engineering standards, and ensure our client solutions are robust, scalable, and maintainable.

• Own the productionization of machine learning models, ensuring they meet performance, reliability, and maintainability standards before deployment.
• Establish and enforce best practices for code review, testing, version control, and documentation across ML projects.
• Identify technical debt and quality gaps in existing solutions and drive improvements.
• Collaborate closely with Data Scientists to understand model requirements and translate experimental code into production-grade implementations.
• Partner with Data Engineers to ensure feature pipelines, data transformations, and model inputs are well-architected and performant.
• Work with DevOps and platform teams to design and implement MLOps workflows including CI/CD, model monitoring, and automated retraining.
• Design and build ML infrastructure and pipelines on AWS using services such as SageMaker, Lambda, Step Functions, ECS/EKS, and related tooling.
• Implement model serving solutions that balance latency, cost, and scalability requirements.
• Develop frameworks and reusable components that accelerate delivery across client engagements.
• Participate in technical discussions with clients to understand requirements and communicate architectural decisions.
• Contribute to solution design and technical proposals.
• Mentor junior team members and help raise the technical bar across the organization.

• 3+ years of experience in software engineering, machine learning engineering, or a related field.
• Strong programming skills in Python with an emphasis on writing clean, testable, well-documented code.
• Hands‑on experience deploying and maintaining ML models in production environments.
• Proficiency with AWS services, particularly those related to data and ML workloads.
• Solid understanding of ML fundamentals including model training, evaluation, feature engineering, and common algorithms.
• Experience with containerization (Docker) and orchestration tools.
• Familiarity with MLOps practices including experiment tracking, model versioning, CI/CD for ML, and monitoring.
• Strong communication skills and the ability to work effectively with cross‑functional teams.

• Experience in a consulting or professional services environment.
• Familiarity with infrastructure‑as‑code tools such as Terraform or CloudFormation.
• Experience with distributed computing frameworks like Spark.
• Background in Data Engineering or Data Science that complements ML Engineering expertise.
• AWS certifications related to Machine Learning or Data Analytics.