AI Data Scientist

Inc Washington

Washington, DC, United States

20 days ago

Role details

Contract type

Temporary to permanent

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

6 years minimum

Working hours

Regular working hours

Job source

bryant-technologies.com

Tech stack

Microsoft Word Agile Methodology Artificial Intelligence Amazon Web Services Amazon Elastic Compute Cloud Amazon S3 Data Analysis Application Frameworks JIRA Microsoft Azure Cloud Computing Continuous Integration

+43 more

Data Visualization R (Programming Language) Monitoring of Systems Information Extraction IT Management Python (Programming Language) Machine Learning Named Entity Recognition Performance Tuning Power BI Search Technologies Software Deployment Software Engineering Tableau (Software) Management of Software Versions Web Application Frameworks Data Logging Cloud Platform System Captcha Flask (Web Framework) 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