

ML Ops Engineer - Woodlawn MD (5 Days a Week Onsite) - Only W2
β - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior ML Ops Engineer in Woodlawn, MD, with a contract length of unspecified duration and a pay rate of "Only W2." Requires strong Python skills, healthcare/claims experience, and a Master's +5 or Bachelor's +7 years of relevant experience.
π - Country
United States
π± - Currency
$ USD
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π° - Day rate
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ποΈ - Date discovered
July 17, 2025
π - Project duration
Unknown
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ποΈ - Location type
On-site
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π - Contract type
W2 Contractor
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π - Security clearance
Unknown
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π - Location detailed
Woodlawn, MD
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π§ - Skills detailed
#Monitoring #AI (Artificial Intelligence) #Debugging #Python #Classification #Logistic Regression #PostgreSQL #Java #Deployment #RNN (Recurrent Neural Networks) #BERT #Containers #Keras #Transformers #Clustering #Version Control #NoSQL #"ETL (Extract #Transform #Load)" #Pandas #PyTorch #SQL (Structured Query Language) #ML (Machine Learning) #GIT #Regular Expressions #TensorFlow #NLP (Natural Language Processing) #ML Ops (Machine Learning Operations) #AWS (Amazon Web Services) #Docker #MongoDB #Libraries #Programming #SageMaker #Regression #Supervised Learning
Role description
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Position β Senior ML Ops Engineer
Location β Woodlawn MD β Onsite 5 days a week
Note: Must be Public Trust obtainable
Interview β 2 rounds; First round Friday at 10:00am (30-minute phone screen with Program Director); assuming that goes well, 2nd round will be with Technical Team next week.
What we need:
β’ Good with onsite interview in Woodlawn, MD
β’ Python programming skills
β’ Good communication
β’ Ideally healthcare/claims/government or insurance experience
β’ MLOPS engineer with strong python programming
Program β The team supports a large, internal Accenture Platform that is used to determine SSA suitability for Medical claims reimbursement. The ML/AI Platforms can scan multiple medical claims simultaneously and apply federal, state, and local Laws and Regulation to determine eligibility. It also can sift through thousands of pages of medical journals, records, claims, etc. to ensure that the treatment being offered to patients and those associated claims for reimbursement makes sense and follow generally accepted medical practice.
MLOps Engineer:
Required Qualifications & Experience:
β’ Masters +5 years of experience or Bachelor's Degree + 7 years of experience
β’ Understanding ML development lifecycle (labeling, training, testing, deployment, monitoring)
β’ Experience deploying high throughput ML models to a production environment. In particular, managing and deploying custom built applications (e.g. with limited to no use of Docker containers)
β’ Expert in Python and dev/test/prod deployment pipelines
Requires proficiency in:
β’ Java
β’ Query languages such as SQL (PostgreSQL) and NoSQL (MongoDB)
β’ Other programming concepts and tools such as regular expressions and version control (Git)
β’ Experience managing ML model performance, such as memory management and debugging
β’ Experience with ML frameworks: Tensorflow, Pytorch, ONNX, Scikit-Learn, Pandas, Keras, Tesseract, Sagemaker, AWS++6
β’ Understanding of ethical AI principles
β’ Excellent oral and written communication skills, and time management skills
β’ Formulate and rapidly prototype various approaches as well as effectively communicate the pros and cons of each.
β’ Ability to contribute to a high-performing, motivated workgroup by applying interpersonal and collaboration skills to achieve project goals
β’ Provide technical guidance in the fields of NLP, Machine Learning, Statistical Methods
β’ Provide data-driven approaches to tackle various business and NLP problems
β’ Experience with statistical model building (particularly classification)
β’ Ability to leverage domain knowledge as well as methods to improve model performance
The following skills are not required but are highly desired:
β’ Experience with NLP and ML technologies and concepts such as: BERT, CNN, RNN, SVMs, k-Nearest Neighbours, Linear/Logistic Regression and Classification, Ensemble Methods, Graphical Models, Clustering, MLPs, Transformers, N-gram, Skipgram
Knowledge of and experience using various NLP approaches, particularly:
β’ NLP preprocessing steps such as tokenization and vectorization
β’ Pattern recognition/feature extraction (N-Gram, Skipgram, etc.)
β’ Supervised, Unsupervised, and Semi-Supervised learning techniques
β’ Practical experience leveraging open source libraries for emerging approaches to NLP
β’ Chunking/Tokenization
β’ Semantic parsing
β’ Information extraction
β’ Experience building, deploying, and maintaining infrastructure and pipelines for LLMs.
β’ Experience integrating prompt management into pipelines.
β’ Understanding of challenges such as token usage, cost, hallucination rates.