AI/ML Engineer

AI/ML Engineer

Posted 6 days ago by 1756365572

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Negotiable
Outside
Hybrid
USA
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A/B Testing Agile Methodology Airflow Amazon Web Services (AWS) Apache Airflow Apache Kafka Apache Spark Application Programming Interface (API) Artificial Intelligence AWS Glue AWS SageMaker Azure Databricks Azure Kubernetes Service Batch Processing Business Requirements Cloud Computing Cloud Services Cloud Technology Containerisation Continuous Delivery Continuous Integration and Continuous Delivery Data Analysis Databricks Data Governance Data Ingestion Data Integrity Data Processing Data Quality Data Science Deep Learning Device Tracking Software Distributed Computing Docker (Software) Feature Engineering Generic Programming Git (Version Control System) Github Google Cloud Google Cloud Platform (GCP) Jenkins Kubernetes Machine Learning Microsoft Azure Model Validation Natural Language Processing NumPy Open Source Development Pandas (Python Package) Python (Programming Language) PyTorch (Machine Learning Library) Redis Root Cause Analysis Scikit-learn (Machine Learning Library) Software Development Software Engineering SQL (Programming Language) Stakeholder Management TensorFlow Terraform Unstructured Data Unsupervised Learning Verification And Validation Version Control Workflows

Summary: The AI/ML Engineer role involves designing, developing, and deploying machine learning models and AI solutions for enterprise applications. The position requires collaboration with cross-functional teams to gather requirements and optimize ML pipelines while ensuring model accuracy and reliability. Candidates should have extensive experience in machine learning engineering and proficiency in relevant programming languages and tools. The role offers flexibility in working arrangements, either remotely or in Florida City, Florida.

Key Responsibilities:

  • Design, develop, and deploy machine learning models and AI solutions for enterprise-scale applications across structured and unstructured data.
  • Collaborate with cross-functional teams to gather business requirements, perform data exploration, and define model objectives and success metrics.
  • Build and optimize end-to-end ML pipelines, including data ingestion, preprocessing, model training, evaluation, and deployment.
  • Perform advanced data analysis and feature engineering on large datasets using Python, SQL, and distributed computing tools.
  • Integrate ML solutions into production environments via REST APIs, cloud services, or batch processing frameworks.
  • Conduct model validation, A/B testing, and performance monitoring to ensure accuracy, fairness, and reliability in production.
  • Use tools like MLflow, Airflow, Git, and Docker for experiment tracking, workflow orchestration, and version control.
  • Work in Agile or hybrid Agile/Waterfall teams, contributing to planning, retrospectives, and continuous delivery practices.
  • Troubleshoot model drift, data quality issues, and production model failures with a focus on root cause analysis and resilience.
  • Collaborate on AI/ML modernization initiatives using MLOps, cloud-native technologies, and scalable model serving infrastructure.

Key Skills:

  • 10 years of experience in machine learning engineering or applied data science.
  • Strong proficiency in Python, SQL, and libraries such as scikit-learn, pandas, NumPy, and TensorFlow or PyTorch.
  • Experience with designing, training, and deploying supervised and unsupervised learning models.
  • Proficient in data processing tools and platforms (e.g., Spark, Databricks, or AWS Glue).
  • Solid understanding of ML model lifecycle, software engineering best practices, and CI/CD for ML.
  • Experience deploying ML models via REST APIs, containers, or serverless cloud functions.
  • Familiar with cloud platforms like AWS, Azure, or Google Cloud Platform, and services like S3, SageMaker, or Vertex AI.
  • Experience with Git-based workflows, containerization (Docker), and orchestration tools (Airflow/Kubeflow).

Salary (Rate): undetermined

City: Florida City

Country: USA

Working Arrangements: hybrid

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT

Detailed Description From Employer:

Position: AI/ML Engineer
Contract: W2 Only

Responsibilities

  • Design, develop, and deploy machine learning models and AI solutions for enterprise-scale applications across structured and unstructured data.
  • Collaborate with cross-functional teams to gather business requirements, perform data exploration, and define model objectives and success metrics.
  • Build and optimize end-to-end ML pipelines, including data ingestion, preprocessing, model training, evaluation, and deployment.
  • Perform advanced data analysis and feature engineering on large datasets using Python, SQL, and distributed computing tools.
  • Integrate ML solutions into production environments via REST APIs, cloud services, or batch processing frameworks.
  • Conduct model validation, A/B testing, and performance monitoring to ensure accuracy, fairness, and reliability in production.
  • Use tools like MLflow, Airflow, Git, and Docker for experiment tracking, workflow orchestration, and version control.
  • Work in Agile or hybrid Agile/Waterfall teams, contributing to planning, retrospectives, and continuous delivery practices.
  • Troubleshoot model drift, data quality issues, and production model failures with a focus on root cause analysis and resilience.
  • Collaborate on AI/ML modernization initiatives using MLOps, cloud-native technologies, and scalable model serving infrastructure.

Required Skills

  • 10 years of experience in machine learning engineering or applied data science.
  • Strong proficiency in Python, SQL, and libraries such as scikit-learn, pandas, NumPy, and TensorFlow or PyTorch.
  • Experience with designing, training, and deploying supervised and unsupervised learning models.
  • Proficient in data processing tools and platforms (e.g., Spark, Databricks, or AWS Glue).
  • Solid understanding of ML model lifecycle, software engineering best practices, and CI/CD for ML.
  • Experience deploying ML models via REST APIs, containers, or serverless cloud functions.
  • Familiar with cloud platforms like AWS, Azure, or Google Cloud Platform, and services like S3, SageMaker, or Vertex AI.
  • Experience with Git-based workflows, containerization (Docker), and orchestration tools (Airflow/Kubeflow).

Nice-to-Have

  • Hands-on experience with deep learning, NLP, or generative AI frameworks (e.g., Hugging Face Transformers, LangChain).
  • Exposure to vector databases (Pinecone, FAISS) or retrieval-augmented generation (RAG) pipelines.
  • Familiarity with Kubernetes, Terraform, or CI/CD tools like Jenkins, GitHub Actions.
  • Knowledge of responsible AI principles, model fairness, and data governance.
  • Understanding of real-time inference systems using Kafka, Redis, or similar platforms.

Soft Skills

  • Strong analytical and problem-solving skills backed by hands-on experience in delivering production-grade ML solutions.
  • Excellent communication skills to translate complex technical concepts into business value for diverse stakeholders.
  • Proven ability to work independently and collaboratively in fast-paced, evolving environments.
  • Passion for continuous learning, open-source contributions, and staying up to date with AI/ML advancements.
  • Detail-oriented mindset with a strong commitment to data integrity, security, and ethical AI practices.

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