AI Data Scientist

Inc Washington
Washington, United States of America
2 days ago

Role details

Contract type
Temporary to permanent
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Remote
Washington, United States of America

Tech stack

Microsoft Word
Agile Methodologies
Artificial Intelligence
Amazon Web Services (AWS)
Amazon Web Services (AWS)
Amazon Web Services (AWS)
Data analysis
Application Frameworks
JIRA
Azure
Cloud Computing
Continuous Integration
Data Visualization
R
Monitoring of Systems
Information Extraction
IT Management
Python
Machine Learning
Named Entity Recognition
Performance Tuning
Power BI
Search Technologies
Software Deployment
Software Engineering
Tableau
Management of Software Versions
Web Application Frameworks
Data Logging
Cloud Platform System
Captcha
Flask
Delivery Pipeline
Large Language Models
Prompt Engineering
Software Security
Model Validation
Generative AI
Cloudformation
Matplotlib
Containerization
Scikit Learn
Kubernetes
XGBoost
Plotly
Machine Learning Operations
Functional Programming
Text Analysis
Cloudwatch
Spacy
Streamlit Framework
Terraform
Document Classification
GPT
Docker

Job description

  • Research, design, and develop machine learning and generative AI solutions, including proof-of-concept prototypes transitioning into production applications
  • Design and implement applications leveraging large language models (LLMs) for text analysis, summarization, information extraction, document classification, and workflow automation
  • Develop prompt engineering strategies and retrieval-augmented generation (RAG) systems to improve AI application performance
  • Build, deploy, and maintain AI/ML models in cloud environments (AWS, Kubernetes), managing end-to-end deployment independently or collaboratively
  • Develop interactive dashboards and analytical applications using Python frameworks (Streamlit, Dash, Flask) or R Shiny
  • Manage deployment pipelines including containerization (Docker), CI/CD practices, and GenAI API integrations with cost optimization
  • Implement monitoring, logging, alerting, and dashboards for model performance, data quality, and system health
  • Communicate technical concepts effectively to both technical and non-technical audiences through presentations, reports, and executive summaries
  • Apply responsible AI practices including fairness evaluation, bias detection, and model interpretability
  • Support governance documentation including system security plans, privacy impact assessments, and authority to operate processes
  • Contribute to building an AI/ML practice through documentation, capability development, and mentoring team members

Requirements

  • Minimum 6 years of hands-on experience developing, deploying, and maintaining AI/ML applications within a large professional or academic organization
  • Bachelor's degree in Computer Science, Data Science, Statistics, Machine Learning, or related field (Master's degree preferred)
  • Expert proficiency in Python or R for data science development; experience with additional programming languages a plus
  • Production deployment experience: ability to build, deploy, and maintain AI/ML applications in cloud environments, including containerization and basic CI/CD practices
  • Proficiency building interactive applications and dashboards using frameworks such as Streamlit, Dash, Flask, or R Shiny
  • Strong experience creating visualizations and dashboards using Python/R libraries, Tableau, Power BI, or similar tools
  • Advanced knowledge of machine learning, NLP (Named Entity Recognition, POS tagging, word embeddings), and Generative AI technologies; experience with Scikit-learn, SpaCy, XGBoost
  • Advanced knowledge of statistical modeling, data analysis techniques, and problem-solving skills
  • Ability to work independently and collaboratively, taking ownership of solutions from conception through production deployment, * Generative AI & LLM application development: prompt engineering, RAG systems, fine-tuning, model evaluation
  • Cloud deployment: AWS, Kubernetes, containerization (Docker), CI/CD pipelines
  • Application frameworks: Streamlit, Dash, Flask, R Shiny
  • Data visualization: Plotly, Matplotlib, Seaborn, ggplot2, Tableau, Power BI
  • LLM APIs and frameworks: GPT, Llama, LangChain, LlamaIndex; vector databases and semantic search
  • AWS AI services: Amazon Bedrock, SageMaker, Comprehend, Rekognition, Transcribe
  • AWS deployment services: EC2, ECS, Lambda, S3, CloudWatch
  • Infrastructure as code: Terraform, CloudFormation
  • MLOps practices: model monitoring, versioning, automated retraining, and deployment pipelines
  • Responsible AI practices: bias detection, fairness evaluation, and model interpretability
  • Agile project tracking tools: Jira, Azure DevOps
  • Federal IT governance frameworks: FISMA, privacy requirements, and application security in regulated environments

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