25.03.2024 aktualisiert
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Principal Engineer, Lead Engineer, Senior SRE, ML-Ops Engineer
berlin, Deutschland
Weltweit
master's degree in radiophysicsSkills
JavaJavaScriptPHPAPIsKünstliche IntelligenzAmazon Web ServicesData AnalysisWeb Application FirewallAtlassian ConfluenceAtlassian JiraNawkBug-TrackingC++UbuntuCloud Computing
awk, Amazon Redshift, AWS, AngularJS, Ansible, Kafka, Web Application Firewalls, APIs, AI, backend, Blockchain, bug tracking system, C++, caffe, celery, cloud infrastructure, cloud, Cloud Security, CloudFlare, cluster analysis, codebase, Confluence, CI/CD, Continuous Integration, ControlNet, data analytics, data centers, databases, Database Technologies, Datadog, DevOps Tools, devops, distributed systems, Django, Docker, Docker Swarm, Elasticsearch, ETL, FastAPI, Flask, FreeBSD, frontend, GPT, generative AI, Git, GitHub, GitLab, GitlabCI, golang, Grafana, GraphQL, grep, Groovy, Hashicorp, Huggingface, Data Engineer, IDS, Istio, Jira, Java, Javascript, Jenkins, Jupyter Notebook, keras, KVM, Kubernetes, k8s, Linux, load balancer, load testing, LXC, Machine Learning Infrastructure, machine learning, memcached, memcache, micro-services, micro-service, MongoDB, MySQL, Neo4j, servers, NodeJS, NumPy, open source, opensource, open stack, PHP, Pandas, Perl, PostgreSQL, Prometheus, Python, PyTorch, RabbitMQ, Redis, system reliability, RESTful APIs, Scikit-learn, SCRUM, SIEM, sed, Snowflake, design patterns, software development, Software Engineering, enterprise software development, software solutions, Splunk, Springboot, Stable Diffusion, svn, Symfony, system architecture, teamcity, TensorFlow, Terraform, theano, Typescript, Linux (ubuntu), Unix command, web-based, web applications, web services, Wiki, xgboost
Erfahrung
Berufserfahrung0 - 5 Jahre
Freelancer seit2024
Sprachen
DeutschGrundkenntnisseEnglischgutFranzösischGrundkenntnisseRussischgutSpanischGrundkenntnisse
Projekthistorie
Responsibilities:
* Building complex, distributed, large scale platform with a strong
understanding of the developer tooling landscape: app instrumentation for
observability, application distributed tracing
* Ownership of cloud infrastructure and growing the devops culture in service
oriented environment
* Design, architect, implement and oversee the technical implementation of
scalable services, telemetry collection and data reporting
* Improvement and maintenance of the tooling around our product life cycle
* Using best practice design patterns and design principles
* Implement, and maintain ML deployment pipelines, ensuring scalable and
efficient model serving across diverse environments.
Technologies:
* Kubernetes, MLFlow, Kubeflow
* AWS, CloudFlare
* Infrastructure as code with tools like: Terraform, Ansible, etc.
* Integrating and maintaining monitoring solutions: Prometheus, Datadog
* TensorFlow, PyTorch, pandas
* Kafka, MySQL, MongoDB
* Best engineering practices
* Building complex, distributed, large scale platform with a strong
understanding of the developer tooling landscape: app instrumentation for
observability, application distributed tracing
* Ownership of cloud infrastructure and growing the devops culture in service
oriented environment
* Design, architect, implement and oversee the technical implementation of
scalable services, telemetry collection and data reporting
* Improvement and maintenance of the tooling around our product life cycle
* Using best practice design patterns and design principles
* Implement, and maintain ML deployment pipelines, ensuring scalable and
efficient model serving across diverse environments.
Technologies:
* Kubernetes, MLFlow, Kubeflow
* AWS, CloudFlare
* Infrastructure as code with tools like: Terraform, Ansible, etc.
* Integrating and maintaining monitoring solutions: Prometheus, Datadog
* TensorFlow, PyTorch, pandas
* Kafka, MySQL, MongoDB
* Best engineering practices
* Using modern approaches to build resilient, scalable and supportable multitenant
infrastructure in public clouds (mainly AWS)
* Immutable infrastructure for production and disposable for testing (or similar
needs)
* Design and build large-scale distributed systems and optimize existing
architecture
* Solving scalability and resilience problems using gitops, infrastructure as
code, micro-services, container orchestration and best DovOps practices
* Applying my software development experience to bring the automation to a
new level
* Setup a full chain of CI/CD as a code (migrated from "Gitlab + GitlabCI" to
"GitHub + ConcourseCI").
Closely working with "Security" department:
* Design the infrastructure compartmentalization
* Using SIEM tools for detecting threats and protection from attacks
* Participating in 27001 certification
* Working on the security "chain of trust"
Closely working with "Deep tech" department:
* Assessment ML/AI services and tools that could perform liveliness human
detection. Assessment was made with Python Pandas project and Tableo.
Top technologies: Hashicorp stack (Vault, Consul, Terranform, Nomad and
other), python, golang and best practices.
infrastructure in public clouds (mainly AWS)
* Immutable infrastructure for production and disposable for testing (or similar
needs)
* Design and build large-scale distributed systems and optimize existing
architecture
* Solving scalability and resilience problems using gitops, infrastructure as
code, micro-services, container orchestration and best DovOps practices
* Applying my software development experience to bring the automation to a
new level
* Setup a full chain of CI/CD as a code (migrated from "Gitlab + GitlabCI" to
"GitHub + ConcourseCI").
Closely working with "Security" department:
* Design the infrastructure compartmentalization
* Using SIEM tools for detecting threats and protection from attacks
* Participating in 27001 certification
* Working on the security "chain of trust"
Closely working with "Deep tech" department:
* Assessment ML/AI services and tools that could perform liveliness human
detection. Assessment was made with Python Pandas project and Tableo.
Top technologies: Hashicorp stack (Vault, Consul, Terranform, Nomad and
other), python, golang and best practices.
* Making micro-service infrastructure play well together
* Lifting to the cloud with immutable infrastructure-as-code and self-service
tools
* Assist the architectural redesign to increase system reliability to operate at
scale
* Eliminating toil by automating repetitive tasks
* Coaching the software engineers on how to use the platform infrastructure
Tech Stack:
* Kubernetes (with Istio/Envoy), Docker Swarm and Docker
* Public Cloud AWS
* Python, Golang, Groovy
* Automation Tools (Ansible, Salt and Terraform)
* Kafka, RabbitMQ, PostgreSQL, Redis
* Monitoring Tools (Splunk, Grafana, Prometheus and OpsGenie)
* Lifting to the cloud with immutable infrastructure-as-code and self-service
tools
* Assist the architectural redesign to increase system reliability to operate at
scale
* Eliminating toil by automating repetitive tasks
* Coaching the software engineers on how to use the platform infrastructure
Tech Stack:
* Kubernetes (with Istio/Envoy), Docker Swarm and Docker
* Public Cloud AWS
* Python, Golang, Groovy
* Automation Tools (Ansible, Salt and Terraform)
* Kafka, RabbitMQ, PostgreSQL, Redis
* Monitoring Tools (Splunk, Grafana, Prometheus and OpsGenie)