AI/ML Engineer: Turn Prototypes into Production

Black Canyon Consulting; Bethesda (MD); On-site; USD 120,000 - 180,000; Full time

Job summary

Black Canyon Consulting is seeking an AI/ML Engineer to design, develop, and maintain ML systems and applications. You will work with data science prototypes, select algorithms, and train models to meet performance targets.

The role involves collaborating with technical and business stakeholders to translate client challenges into scalable AI-enabled capabilities. Responsibilities include building production-ready ML pipelines, evaluating models, and researching appropriate algorithms and tools.

Qualifications

• Experience developing and maintaining ML models and systems.
• Strong ML algorithms, math, probability, statistics knowledge.
• Proficiency with Python, Java, or R.
• Experience with ML frameworks PyTorch or Keras.

Responsibilities

• Design, develop, and implement machine learning systems and applications.
• Create, train, evaluate, fine-tune, and retrain machine learning models.
• Research and implement algorithms, frameworks, and tools for ML.
• Transform data science prototypes into scalable ML solutions.
• Select datasets and representations based on project requirements.
• Conduct ML tests and experiments and evaluate model performance.
• Extend and integrate ML libraries and frameworks as needed.
• Collaborate with stakeholders to identify AI-enabled opportunities.

Skills

ML models

Python

Java

R

Statistics

Data modeling

Experimentation

Cross-functional

Problem solving

Tools

PyTorch

Keras

scikit-learn

TensorFlow

Job description

Black Canyon Consulting is seeking an AI/ML Engineer to design, develop, and maintain ML systems and applications. You will work with data science prototypes, select algorithms, and train models to meet performance targets.

The role involves collaborating with technical and business stakeholders to translate client challenges into scalable AI-enabled capabilities. Responsibilities include building production-ready ML pipelines, evaluating models, and researching appropriate algorithms and tools.