AI, Developer

AI in this role

bedrockpytorchtensorflowscikit-learnmlflowsagemakerdatabricks

ml-opscomputer-visionnlpai-safety

Master Works is seeking an AI, Developer to lead the development of intelligent solutions leveraging Artificial Intelligence technologies, with a focus on Generative AI and advanced analytics.

The successful candidate will transform data into valuable business insights and develop AI-powered applications that drive smarter decision-making, improve operational efficiency, and support digital transformation initiatives.

Key Responsibilities:

Design, develop, train, and optimize machine learning models for real applications or use cases.

Translate business and product requirements into scalable ML/AI solutions.

Implement feature engineering, model selection, tuning, and evaluation techniques.

Develop, and deploy ML models into production environments with high availability and performance.
Build and maintain ML pipelines (training, validation, deployment, monitoring).

Monitor model performance, data drift, and model decay; retrain models as needed.

Ensure models meet reliability, scalability, and security standards.

Work closely with Data Scientists, Product Managers, and Software Engineers.

Collaborate with data engineering teams to ensure high-quality, reliable data pipelines.

Participate in design and code reviews, ensuring engineering best practices.

Optimize models for latency, throughput, and cost.

Implement experimentation frameworks (A/B testing, offline evaluation).

Apply responsible AI principles, including fairness, explainability, and governance where required.

Requirements

Requirements & Qualifications:

+4 years of hands-on experience in Machine Learning or applied AI roles.
Strong programming skills in Python (and/or Java, Scala).

Solid understanding of ML algorithms (supervised, unsupervised, deep learning).

Experience with frameworks such as TensorFlow, PyTorch, Scikit-learn.

Experience deploying models using Docker, Kubernetes, or cloud ML services.

Strong knowledge of data structures, algorithms, and software engineering principles.

Experience working in agile, cross-functional teams.

Experience with cloud platforms (AWS, Azure, or GCP) and managed ML services.

Hands-on experience with MLOps tools (MLflow, Kubeflow, Airflow, SageMaker, Azure ML).

Experience with big data technologies (Spark, Kafka, Databricks).

Background in NLP, Computer Vision, or Generative AI.

Strong problem-solving and analytical thinking

Production-first mindset

Data-driven decision making

High Collaboration and communication skills

Tools & Technologies:

Dataiku
SQL server
Power bi,
Aws bedrock

Job Location: Client site

How we rate this

AI, Developer at Master-Works rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ML OpsComputer VisionNLPAI SafetyBedrockPyTorchTensorFlowscikit-learn

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
What NLP problem have you worked on, and how did you measure whether it actually worked?
How do you think about the risk of an AI system in this kind of role failing silently?
Walk me through how you've used Bedrock in your day-to-day work.

Adapt your resume

List these exact terms on your resume: ML Ops, Computer Vision, NLP, AI Safety, and Bedrock. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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