Direct client need - Machine Learning Engineer at Irving, TX (2 days from office - hybrid model) - *** Need only 12+ Years overall experience candidates***

  • Irving, TX
  • Posted 6 days ago | Updated 6 hours ago

Overview

Hybrid
$60 - $75
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month(s)

Skills

Artificial Intelligence
Machine Learning (ML)
Continuous Integration

Job Details

Greeting from Accionlabs;

Job Description: Machine Learning Engineer (Multi-Modal Retrieval & Conversational AI)
Location: Dallas, Texas ( 2 days from office and rest of the days from home)
About the Role
We are looking for a Machine Learning Engineer to design and implement AI systems that combine computer vision, NLP, and structured data for retrieval, similarity search, and conversational interactions. You will work on multi-modal pipelines that extract features, perform matching, and interact with end-users through schema-driven conversational flows.
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).
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