Direct Client Position-Machine Learning Engineer-Hybrid-2 Days Onsite -Irving, TX

Role: Machine Learning Engineer Location: 2days Onsite from Irving, TX Type of position: Contract Number of positions: 2 Job Description: Machine Learning Engineer (Multi-Modal Retrieval & Conversational AI) 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.