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Senior Machine Learning Engineer

hace 4 meses

Buenos Aires, Argentina Blue Orange Digital A tiempo completo

Company Overview:

Position Overview:


This role involves direct interaction with our clients, providing expert guidance to design, implement, and optimize machine learning models for production environments and contributing to the scaling and maintaining robust data platforms.


This is a full-time fully remote position for Latin American-based applicants within +/- 2 hours of Eastern Standard Time Zones (NYC).

Resume must demonstrate professional English ability.


At Blue Orange, you'll have the opportunity to work on cutting-edge projects, leveraging modern machine-learning and AI techniques to deliver tangible business outcomes and drive innovation in our data-driven solutions.


Responsibilities:

Develop and Implement Machine Learning and AI Models:

  • Design, build, deploy, and monitor advanced machine learning models.
  • Improve model performance by conducting feature engineering, hyperparameter search, and metric selection.
  • Optional: Experience working with classical

NLP:
Intent recognition, Named Entity Recognition (NER), and Part of Speech Tagging (POS). Using Sklearn, Spacy, and Hugging Face.

  • Build LLMbased products and stay up to date with current developments. Proficiency using Hugging Face, OpenAI, Anthropic, and/or Cohere tools. Experience working with Deep Learning frameworks Pytorch and/or Tensorflow.
  • Design and build custom APIs with tools like FastAPI.
  • Finetune LLMs using GPUs/TPUs in cloud environments and then deploy them with tools such as vLLM, HuggingFace Inference Endpoints, and/or AWS Sagemaker. Use tools like Skypilot for cloudagnostic deployments.
  • Build LLM orchestration systems with tools like LangChain, LLamaIndex, Semantic Kernel, and/or HayStack.
  • Build predictive analytics and modeling products using tools like Sklearn, Sktime, XGboosts, and/or LightGBM.
-
Data Analytics and Processing:

  • Analyze large, complex datasets to extract actionable insights and inform model development.
  • Implement data preprocessing, cleansing, and quality checks to ensure data quality.
  • Experience meeting ambitious customer's expectations is required. Direct customer expectation management is a plus.
  • DBT is a plus.
  • Working with distributed computing tools like Spark or Dask in AWS EMR or GLUE, Azure DataBricks, and GCP BigQuery.

-
End-to
End ML/AI Cycle Management:Oversee the entire machine learning lifecycle from ideation, data collection, and model development to deployment and performance monitoring. Collaborate with crossfunctional teams to integrate machine learning solutions into the company's products and services. Build productiongrade systems with orchestration tools like Airflow, Prefect, and/or Dagster.-
Cloud-Native Solutions and MLOps:

  • Develop and maintain scalable, cloud-native machine learning solutions using AWS (Lambda, EMR, GLUE, ECS, EKS), GCP (GKE, Anthos, Cloud Run), and/or Azure (CA, KS).
  • Implement and manage MLOps practices to automate and streamline the ML model deployment process. Using tools such as MLflow, Kubeflow, Metaflow, and/or Weights and Biases for storing metrics, artifacts, and experiments.
-
Containerization Technologies:

  • Utilize containerization technologies like Docker, Dockercompose, and/or Kubernetes to ensure consistent and scalable deployment of machine learning models. Using FastAPI microservices.
-
Innovation and Continuous Learning:

  • Stay up-to-date with the latest trends and advancements in machine learning and AI technologies.
  • Drive innovation within the team by exploring new methodologies and techniques for problemsolving.
-
Quality Assurance and Best Practices:

  • Ensure the highest quality of machine learning models through rigorous testing and validation. Using unit and integration testing with CI/CD pipelines through GitHub actions.
  • Advocate and adhere to best software (i.e., SOLID, DRY, Git version control, etc.) and machine learning (train, val, test data splits, baseline definition, overfitting management, etc) within the team.

Requirements:

  • Degree in Computer Science, Engineering, Mathematics, or a related field.
  • Strong mathematical skills, particularly in statistics and linear algebra.
  • Extensive knowledge of deep learning frameworks and ML libraries.
  • Experience with NLP and LLMbased technologies and frameworks.
  • Proficiency in programming languages such as Python,
  • Experience with cloudbased technologies AWS, GCP, and/or Azure.
  • Experience with big data technologies like Spark and/or Dask
  • Expertise in the EndtoEnd ML cycle.
  • Strong problemsolving and analytical skills.
  • Selfdriven and autonomous.
  • Excellent verbal and written communication skills
  • Team player.
  • Eagerness to learn and adapt in a fastpaced environment.
  • Propositive and creative.

Preferred qualifications:

  • Advanced degree in a relevant field.
  • Publications in relevant AI/ML communities and journals.

Benefits:

  • Fully remote
  • Flexible Schedule
  • Unl