Direct Client !! Sr. Machine Learning Engineer - Irving, TX (Hybrid Work)

  • Irving, TX
  • Posted 10 hours ago | Updated 10 hours ago

Overview

Hybrid
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - 6 Month(s)

Skills

Machine Learning (ML)
Machine Learning Operations (ML Ops)
Continuous Integration
Artificial Intelligence
Microsoft Azure
PyTorch
Python
TensorFlow
Data Extraction
Docker

Job Details

We are Hiring Sr. Machine Learning Engineer candidates with OCR + NER pipelines, LLM Integration and Vector Databases experience at Irving, TX (Hybrid Work) for 6+ Months of Contract.
Job Description: Machine Learning Engineer (Multi-Modal Retrieval & Conversational AI)
Location: Irving, T X Hybrid Work (2 Days office and 3 Dyas remote)
Duration: 6+ Months
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).
What We Offer
  • Opportunity to build multi-modal AI retrieval systems combining image, text, and structured data for high-impact real-world workflows.
  • Exposure to vector search, embeddings, and conversational AI with schema-driven interactions.
  • Collaborative, fast-paced environment where ML engineers have end-to-end ownership of retrieval and conversational pipelines.
  • Growth opportunities into retrieval-augmented AI, conversational system design, or MLOps specializations.
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