Machine Learning Engineer
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We are seeking a highly skilled Machine Learning Engineering Consultant to join a collaborative AI/ML engineering team focused on building scalable, enterprise-grade AI solutions. This role is ideal for someone with strong hands-on ML engineering experience, cloud infrastructure expertise, and a passion for solving complex business problems with modern AI technologies. This team operates in a highly collaborative POD-based environment, and they are looking for someone who is excited to contribute onsite alongside the team several days per week.
What You’ll Be Doing:
- Design, build, deploy, and monitor scalable ML pipelines and AI solutions
- Develop and operationalize machine learning models in production environments
- Collaborate with data scientists, software engineers, and technical stakeholders to translate AI research into real-world applications
- Build robust and scalable cloud-native ML infrastructure and workflows
- Optimize ML models through tuning, testing, and continuous improvement
- Support CI/CD automation and DevOps processes for ML applications
- Evaluate emerging AI/ML tools, frameworks, and technologies
- Contribute to architectural decisions and mentor junior engineers when needed
Required Skills & Experience:
- 5+ years of Machine Learning Engineering experience in production environmentsStrong Python programming skills
- Advanced SQL expertise
- Hands-on AWS cloud engineering experience
- Experience with:
- Deep expertise in Core ML Engineering, including advanced Python programming, advanced SQL, and experience building scalable ML pipelines and AI solutions
- Strong AWS-focused Cloud & Infrastructure experience, including cloud-native application deployment and scalable ML architecture
- Proven experience with DevOps, CI/CD pipelines, automation, and Infrastructure as Code
- Demonstrated experience implementing and supporting real-world AI/ML solutions in production environments
- Preferred / Nice-to-Have SkillsAmazon Bedrock
- Amazon SageMakerAWS EKS / KubernetesAWS Step Functions
- Apache Airflow
- DockerMLOps frameworks and practicesCI/CD and DevOps automation experience
- Infrastructure as Code experience with tools such as Terraform, Cloud
- Formation, Puppet, or GitHub workflows
- Experience deploying ML models into production environmentsStrong understanding of scalable system architecture and software engineering principles
- What They’re Looking ForStrong technical depth in ML Engineering and AI solutions
- Someone comfortable owning work end-to-end with minimal oversight
- Excellent communication and collaboration skills
- A team-oriented engineer who enjoys working closely with others onsite
- Experience working in Agile, fast-paced engineering environments