Start Date
Immediate
Expiry Date
19 Nov, 25
Salary
0.0
Posted On
20 Aug, 25
Experience
5 year(s) or above
Remote Job
Yes
Telecommute
Yes
Sponsor Visa
No
Skills
Sentiment Analysis, Python, Machine Learning, Risk Modeling, Metrics, Compliance Monitoring, Fraud Detection
Industry
Information Technology/IT
August 19, 2025
Role: AI/ML Engineer (ML Ops) – Model Deployment & Optimization
Employment Type: C2C
Client: REI Systems – FDA Account
Location: Remote, USA
No. of Positions: 1
Work Experience: 5+ Years of experience
Educational Qualifications: BS in Engineering, Computer Science, Information Systems or equivalent
Visa & Work Authorization: US Citizen/Green Card Holder/H1B/GC-EAD
Residency: Must have lived in the U.S. for at least 1095 days (3 years).
Security Clearance Requirements: Eligible to obtain Public Trust Clearance
Work Location:Hybrid or Remote for very strong candidates
Contract Duration: 6 months, extendable up to 1 year.
Employment Type: C2C
Interview Process: Two technical rounds (Including 1 coding round) followed by client interaction – Virtual Interviews
SKILLS & EXPERIENCE REQUIREMENTS:
5+ years applying advanced statistical, machine learning, and graph analytics techniques to solve complex risk or anomaly detection problems.
Strong proficiency in Python, including ML frameworks (PyTorch, TensorFlow, scikit-learn) and graph ML libraries.
Experience with transformer-based NLP models, sentiment analysis, and entity resolution.
Ability to design and evaluate predictive models using metrics like Precision@K, ROC-AUC, F1-score, with a focus on operational impact.
Demonstrated experience in translating model insights into clear, explainable outputs for non-technical stakeholders.
PREFERRED QUALIFICATIONS:
Hands-on work in fraud detection, compliance monitoring, or national security risk modeling.
Familiarity with imports risk screening workflows, PREDICT, or similar systems.
Experience modeling complex supply chains and applying graph-based ML to entity relationships.
Prior exposure to FDA or other federal public health agency datasets.
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