PLEASE
NOTE:

• 
  It
  is 100% onsite position in Woodlawn, MD.
• 
  Candidate should be local and ready to work on onsite 5 days a week at Client HQ in Woodlawn, MD.
• 
  Candidate
  must be able to obtain and maintain a public trust clearance
• 
  ​
  Interviews will be scheduled quickly for early next week. There will only be one round of interview

Position
Description:

• 
  Hands on experience in Python, NLP
  frameworks, SQL, Pandas, NLTK, SPACy and LLMs
• 
  Well versed in SQL and analyzing trends and
  transactional data.
• 
  Understand real world challenges and develop
  automated data solutions
• 
  Develop, test, and deploy new techniques for
  NLP understanding
• 
  Scalable development/deployment of ML and
  Generative AI approaches (such as Large Language Models (LLMs)
• 
  Train and optimize NLP/LLM models and create
  Python based pipelines
• 
  Experience building cloud native solutions on
  AWS
• 
  Determine the nature of analytic problems,
  evaluate options, and offer recommendations for resolution.
• 
  Advise on the methods and data needed and/or
  available to evaluate the (intelligence or data) problem.
• 
  Collaborate with data collectors and analysts
  to identify and close gaps on complex monitoring problems.
• 
  Provide accurate, timely, complex, and
  sophisticated data analysis.

Key
Required Skills:

• 
  Solid Experience with Natural Language
  Processing (NLP), Python, NLP frameworks, SQL, Pandas, NLTK and SPACy.
• 
  Experience with Generative AI and Large
  Language Models (LLM)
• 
  Excellent Communication skills.

Skills
Requirements:

Basic
Qualification:

• 
  Master's and 10+ years of experience,
  Bachelor's and 12+ years of experience or 18+ years in lieu of a degree
• 
  Bachelor’s degree in Statistics, Applied
  Mathematics, Computer Science, or Information Science with industry experience
  on Python, NLP frameworks, SQL, Pandas, NLTK and SPACy, data science, and
  AI/ML/LLM engineering.
• 
  Overall 10+ years’ experience in IT industry

Required
Skills:

• 
  Solid Experience with Natural Language
  Processing (NLP), Python, NLP frameworks, SQL, Pandas, NLTK and SPACy.
• 
  Experience with Generative AI and Large
  Language Models (LLM)
• 
  Evidence of true self-starter and operating
  independently.
• 
  Fluency in Python Programming, version
  control and collaboration with GIT, standard Python packages (ex. Pandas,
  numpy, matplotlib) and ML frameworks
• 
  Knowledge of TensorFlow, PyTorch, Pandas,
  scikit-learn, NLTK, Azure ML (optional), Amazon Web Services EC2.
• 
  Experience with scalable data engineering
  frameworks such as Apache Spark and orchestration frameworks such as Airflow,
  and/or experience with semantic search.
• 
  Expert knowledge in conducting data analysis
  and applying advanced statistical concepts and ML methods to build, train,
  test, and evaluate a variety of supervised and unsupervised analytic models.
• 
  Experience with ML model deployment and
  operations like DevOps, MLOps, LLMOps.
• 
  Experience with NLP and Generative AI
  libraries like regular expressions (e.g., spacy, langchain), text annotation
  tools and semantic frameworks.
• 
  Ability to clean and process large amounts of
  real-world data.
• 
  Experience retrieving and manipulating data
  from a variety of data sources included DB2, Oracle, SQL Server, Hadoop and
  flat files.
• 
  Excellent Communication skills.
• 
  Experience with database management systems
  (e.g., PostgresSQL, MySQL, SQLite, SQL, etc.)
• 
  Excellent analytical skills to identify
  potential risks and propose effective solutions.
• 
  Excellent problem-solving skills, ability to
  collaborate with cross-functional teams and proven communication in written and
  verbal formats to various audiences to include executive leadership.

Desired
Skills:

• 
  Prior experience with federal or state
  governments IT projects.
• 
  Industry experience preferred
• 
  Experience with, or the ability and
  willingness to learn distributed processing via the Hadoop ecosystem, i.e.,
  Spark, Impala and Hive.
• 
  Experience working in an analytical research
  environment.
• 
  Experience in parallel processing such as GPU
  programming with CUDA
• 
  Experience with Mathematica
• 
  Experience using markup languages such as
  LaTeX, HTML, etc.
• 
  Experience with Natural Language Processing
  for anomaly detection
  .