Intellectt Inc
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer focusing on AI/ML, offering a remote contract with a pay rate of "unknown." Key skills required include Python, cloud platforms (Azure, AWS, GCP), and experience in healthcare analytics. A Bachelor’s/Master’s in a related field and 3–7+ years of relevant experience are mandatory.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
January 14, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
New Jersey, United States
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🧠 - Skills detailed
#AWS (Amazon Web Services) #Monitoring #Pandas #Scala #Databricks #Big Data #Programming #PyTorch #NLP (Natural Language Processing) #Computer Science #Data Engineering #Data Science #SageMaker #Cloud #Forecasting #Transformers #Classification #GCP (Google Cloud Platform) #Hugging Face #Deep Learning #MLflow #Hadoop #Spark (Apache Spark) #ML (Machine Learning) #SQL (Structured Query Language) #Python #Langchain #Model Deployment #AI (Artificial Intelligence) #API (Application Programming Interface) #Azure #"ETL (Extract #Transform #Load)" #Microservices #NoSQL #NumPy #Databases #Deployment #TensorFlow
Role description
Role: AI/ML Engineer
Location: Remote
Position Overview
The candidate will be responsible for designing, developing, and deploying machine learning models and AI-driven solutions to support healthcare analytics, improve operational efficiency, and drive business insights. This role requires strong expertise in machine learning, data engineering, cloud platforms, and end-to-end model lifecycle management.
Key Responsibilities
• Develop and implement scalable machine learning models for predictive analytics, classification, NLP, and optimization use cases.
• Work closely with data engineers and analysts to gather requirements, understand business problems, and translate them into ML solutions.
• Perform data preprocessing, feature engineering, model tuning, and validation.
• Build reusable ML pipelines and automate workflows through MLOps frameworks.
• Deploy models into production using cloud-native services (Azure, AWS, or GCP).
• Monitor and optimize model performance and ensure long-term model stability.
• Collaborate cross-functionally with product, engineering, and business stakeholders within Optum.
• Document solution designs, model behavior, and deployment architecture.
Mandatory Skills
• Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow / PyTorch).
• Experience building ML models end-to-end, including training, validation, deployment, and monitoring.
• Hands-on experience with NLP, LLMs, or generative AI technologies (Hugging Face, LangChain preferred).
• Knowledge of cloud platforms such as Azure, AWS, or GCP (Optum widely uses Azure & GCP).
• Familiarity with MLOps tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or Azure ML Studio.
• Strong understanding of data structures, algorithms, and SQL/NoSQL databases.
Good-to-Have Skills
• Experience with deep learning architectures (CNNs, RNNs, Transformers).
• Healthcare domain experience (claims, EHR, risk scoring, utilization forecasting).
• Exposure to big data technologies (Spark, Databricks, Hadoop).
• Experience with API development and microservices for model deployment.
• Understanding of responsible AI, bias detection, fairness and model interpretability (SHAP, LIME).
Qualifications
• Bachelor’s/Master’s in Computer Science, Data Science, AI/ML, or related field.
• 3–7+ years of experience in machine learning or AI development.
Role: AI/ML Engineer
Location: Remote
Position Overview
The candidate will be responsible for designing, developing, and deploying machine learning models and AI-driven solutions to support healthcare analytics, improve operational efficiency, and drive business insights. This role requires strong expertise in machine learning, data engineering, cloud platforms, and end-to-end model lifecycle management.
Key Responsibilities
• Develop and implement scalable machine learning models for predictive analytics, classification, NLP, and optimization use cases.
• Work closely with data engineers and analysts to gather requirements, understand business problems, and translate them into ML solutions.
• Perform data preprocessing, feature engineering, model tuning, and validation.
• Build reusable ML pipelines and automate workflows through MLOps frameworks.
• Deploy models into production using cloud-native services (Azure, AWS, or GCP).
• Monitor and optimize model performance and ensure long-term model stability.
• Collaborate cross-functionally with product, engineering, and business stakeholders within Optum.
• Document solution designs, model behavior, and deployment architecture.
Mandatory Skills
• Strong programming skills in Python (NumPy, Pandas, Scikit-learn, TensorFlow / PyTorch).
• Experience building ML models end-to-end, including training, validation, deployment, and monitoring.
• Hands-on experience with NLP, LLMs, or generative AI technologies (Hugging Face, LangChain preferred).
• Knowledge of cloud platforms such as Azure, AWS, or GCP (Optum widely uses Azure & GCP).
• Familiarity with MLOps tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or Azure ML Studio.
• Strong understanding of data structures, algorithms, and SQL/NoSQL databases.
Good-to-Have Skills
• Experience with deep learning architectures (CNNs, RNNs, Transformers).
• Healthcare domain experience (claims, EHR, risk scoring, utilization forecasting).
• Exposure to big data technologies (Spark, Databricks, Hadoop).
• Experience with API development and microservices for model deployment.
• Understanding of responsible AI, bias detection, fairness and model interpretability (SHAP, LIME).
Qualifications
• Bachelor’s/Master’s in Computer Science, Data Science, AI/ML, or related field.
• 3–7+ years of experience in machine learning or AI development.
