Develops and maintains architectures, pipelines, algorithms, interfaces, models, and code for an MLOps pipeline.
Collaborate with data scientists and ML engineers to operationalize machine learning models, establish model performance metrics, and implement model versioning testing, and validation processes.
Develop scalable, resilient, and secure cloud-based and on-premises infrastructure components to support AI-driven applications and services.
Utilizes modern object-oriented computer programming techniques and agile programming methodologies to develop software in delivered Boeing systems.
Implement observability solutions to monitor, analyze, and optimize application performance, infrastructure utilization, and user experience.
Stay updated on emerging trends and advancements in AI, machine learning, MLOps, DevOps, and full-stack development, and evaluate new technologies for potential adoption.
Works as a member of the International team with the members in one or more countries where English is the common language.
Requirements & Skills:
Experience in ML model serving through APIs in web servers
Proven experience (X years) in AIOps, MLOps, DevOps, or Full Stack development roles, with a focus on automation, infrastructure management, and software engineering.
Strong proficiency in programming languages such as Python, Java, JavaScript, or Go, and experience with frameworks/libraries such as Flask, Django, React, Angular, etc.
Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP), and proficiency in cloud-native services and technologies.
In-depth knowledge of MLOps principles, machine learning frameworks (e.g., TensorFlow, PyTorch), DevOps practices, CI/CD pipelines, and infrastructure-as-code tools.
Familiarity with containerization technologies (e.g., Docker, Kubernetes) and microservices architecture.
Experience with monitoring and observability tools such as Prometheus, Grafana, ELK stack (Elasticsearch, Logstash, Kibana), New Relic, Datadog, etc.
Strong analytical and problem-solving skills, with the ability to troubleshoot complex issues and implement effective solutions.
Excellent communication and collaboration skills, with the ability to work effectively in a fast-paced, dynamic environment.
Relevant certifications such as AWS Certified DevOps Engineer, TensorFlow Developer Certificate, or similar credentials are a plus
Applicants must have the enthusiasm to learn new technologies and share them with others
This role requires creativity, critical thinking, and troubleshooting skills
Proper English communication skills in verbal and written
Advanced degree (e.g. Bachelor, Master, PhD. etc. in Engineering, Computer Science or related field) preferred, but not required.
Nice to have field experience in developing and executing manufacturing projects using machine learning techniques to optimize manufacturing processes, reduce downtime, and improve quality.
Experience in building a large-scale system
Good to have in-depth knowledge in server-side frameworks or libraries such as Databases (MySQL, PostgreSQL, etc), Flask/FastAPI, Kubeflow/BentoML, MLflow, RabbitMQ, Redis, etc
Good to have in-depth knowledge in server engineering like resource management on multi-GPUs over multiple servers
Nice to have your git repository shared with well-organized projects inside
