Overview
On Site
$60,000 - $80,000
Full Time
Skills
Artificial Intelligence
Kubernetes
Python
Continuous Integration
Data Extraction
Machine Learning (ML)
Docker
Extraction
Microsoft Azure
Collaboration
TensorFlow
PyTorch
Job Details
Machine Learning Engineer
Irving, TX (Day 1 Onsite)
Visa: GCEAD And USC
Responsibilities
- Develop and optimize embedding pipelines for image and text similarity (e.g., CLIP, SigLIP, Sentence Transformers).
- Implement vector search and retrieval using FAISS, Pinecone, or pgvector.
- Build feature extraction pipelines (OCR + NER + numeric parsers) to detect schema-defined attributes.
- Design and validate a feature comparison engine to detect missing or low-confidence values.
- Integrate conversational AI agents with slot-filling logic to request missing details from users.
- Apply server-side validation for numeric, categorical, and free-text inputs.
- Track experiments using MLflow / W&B and evaluate with metrics like Recall@k, MRR, F1.
- Deploy retrieval and conversational services on Kubernetes / App Services with CI/CD pipelines.
- Collaborate cross-functionally with engineers and product teams to refine schema and conversational UX.
Qualifications
- Strong knowledge of embeddings and retrieval models for multi-modal data.
- Experience with OCR + NER pipelines for structured data extraction.
- Proficiency in vector databases (FAISS, Pinecone, pgvector, Azure AI Search).
- Familiarity with LLM integration for conversational AI (tooling, slot-filling, schema control).
- Strong Python and PyTorch/TensorFlow skills.
- Hands-on experience with containerized ML services (Docker, Kubernetes).
Preferred
- Experience combining image + text embeddings into unified retrieval pipelines.
- Knowledge of schema-driven conversational AI design.
- Familiarity with monitoring & drift detection tools (Evidently, Prometheus).
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