Machine Learning Engineer

Hire Hangar, Inc.; Argentina; A distancia; ARS 212.224.000 - 288.018.000; Jornada completa

Descripción de la vacante

Hire Hangar, Inc. is seeking a Machine Learning Engineer (Data & AI) to design, train, and deploy ML models and data pipelines for diverse environments.

You will work across data sourcing, cleaning, structuring, model evaluation, and production deployment, emphasizing data quality and scalable inference. The role requires strong Python and ML library experience, hands-on data engineering skills, and familiarity with MLOps tooling.

Formación

• Must have strong Python development experience and hands-on work with core ML libraries (e.g., PyTorch, scikit-learn).
• Solid data engineering background including SQL, ETL, and large-scale datasets.
• Experience with model training, evaluation, hyperparameter tuning, and deployment in production.

Responsabilidades

• Design, build, and maintain robust data pipelines for ingestion and feature engineering.
• Develop, train, evaluate, and iterate ML models across classification, regression, clustering, and NLP tasks.
• Fine-tune pre-trained LLMs for specific use cases and datasets.
• Build and manage MLOps infrastructure including model versioning and deployment pipelines.
• Work with structured and unstructured data at scale and monitor performance in production.

Conocimientos

Python

ML libraries

Data engineering

Experiment design

Remote collaboration

Herramientas

MLflow

Weights & Biases

DVC

Descripción del empleo

Join Hire Hangar and work with fast-growing global companies while building a long-term career.

Job Title: Machine Learning Engineer (Data & AI)

Location: Remote Time Zone: US Time Zones (EST–PST)

Role Overview

We are looking for a skilled Machine Learning Engineer with a strong data engineering foundation to build, train, and deploy ML models and data pipelines across a range of complex environments. This role sits at the intersection of data and AI — you will be responsible for everything from sourcing, cleaning, and structuring data to training models, evaluating performance, and getting solutions into production. The ideal candidate thinks rigorously about data quality, understands the full ML lifecycle, and is equally comfortable working with large datasets as they are fine-tuning models or building scalable inference pipelines.

Key Responsibilities

• Design, build, and maintain robust data pipelines for ingestion, transformation, and feature engineering
• Develop, train, evaluate, and iterate on machine learning models across classification, regression, clustering, and NLP tasks
• Fine-tune and adapt pre-trained LLMs and foundation models for specific use cases and datasets
• Build and manage MLOps infrastructure including model versioning, experiment tracking, and deployment pipelines
• Work with structured and unstructured data at scale — including text, tabular, and time-series data
• Monitor model performance in production and implement retraining and drift-detection strategies
• Collaborate with engineering and product teams to translate data insights into actionable AI features
• Document data schemas, model architectures, and pipeline logic clearly and thoroughly

Required Qualifications

• Strong Python skills with hands‑on experience in core ML libraries (scikit-learn, PyTorch, TensorFlow, or similar)
• Solid data engineering experience — SQL, ETL pipelines, and working with large-scale datasets
• Practical experience with model training, evaluation, hyperparameter tuning, and deployment
• Familiarity with LLMs and transformer-based architectures; experience with fine-tuning or prompt engineering in production contexts
• Experience with experiment tracking and MLOps tooling (MLflow, Weights & Biases, DVC, or similar)
• Strong grasp of statistical concepts, data quality principles, and model performance metrics
• Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered.

Preferred Qualifications

• Experience with distributed data processing frameworks (Spark, Dask, or similar)
• Familiarity with vector databases and embedding-based retrieval systems
• Background working with real-time or streaming data pipelines (Kafka, Flink, or similar)
• Exposure to cloud-native ML platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
• Experience with data governance, lineage tracking, or compliance-aware data workflows

Tools & Technology

• Python, SQL, and core ML/data libraries (PyTorch, scikit-learn, Pandas, NumPy)
• MLOps: MLflow, Weights & Biases, DVC, or equivalent
• Data warehouses and lakes: Snowflake, BigQuery, Redshift, or similar
• LLM platforms: Hugging Face, OpenAI, Anthropic, or similar
• Cloud infrastructure: AWS, GCP, or Azure
• Google Workspace, Slack, Zoom, and remote collaboration tools