Senior ML Engineer

Responsibilities

• Data Pipeline & Knowledge Base Construction:
• Design, implement, and optimize robust pipelines for ingesting, parsing, and extracting structured information from complex documents ( leveraging OCR, document layout analysis, Named Entity Recognition (NER), and Relationship Extraction ( RE)) .
• Develop rich, nested JSON schemas for representing structured data and ensure scalable storage
• Generate and manage high-quality vector embeddings for efficient retrieval-augmented generation (RAG) within a Vector Database.
• Language Model (LM) Development & Fine-tuning:
• Research, select, and experiment with appropriate open-source Language Models (Large & Small) (e.g., Phi-3, Mistral, Llama, Nemotron -H families) for specialized tasks.
• Design and execute efficient fine-tuning strategies (e.g., LoRA , QLoRA , full fine-tuning) on curated, domain-specific datasets to achieve precise performance for tasks like coverage determination, code lookups, and policy rule application.
• Explore and implement knowledge distillation techniques to transfer capabilities from larger models to smaller, more efficient LMs.
• Agentic System Design & Implementation:
• Build and maintain the core agentic framework, including the orchestrator that intelligently routes queries and coordinates interactions between various specialized LM tools.
• Develop and integrate "tools" (specialized LMs and external APIs) that perform atomic medical necessity tasks, ensuring strict behavioral alignment and structured outputs.
• MLOps & Deployment:
• Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using services like Vertex AI, GKE, Cloud Functions, and Cloud Run.
• Implement robust MLOps practices for continuous integration, continuous delivery (CI/CD), model versioning, and performance monitoring (latency, throughput, accuracy).
• Continuous Improvement & Research:
• Establish effective feedback loops from end-user interactions and system logs to identify areas for model improvement.
• Curate and expand training datasets, ensuring data privacy (PHI/PII masking) and legal compliance.
• Stay abreast of the latest research in LMs, agentic AI, NLP, and document understanding, applying relevant advancements to our system.
• Collaboration:
• Work closely with subject matter experts, product managers, and other engineers to translate complex requirements into technical solutions and evaluate system performance.
• Bachelor's or Master's degree in Computer Science , Machine Learning, Artificial Intelligence, or a related quantitative field.
• 3+ years of professional experience in Machine Learning Engineering, with a strong focus on NLP.
• Proven experience with Language Models (LMs), including model selection, fine-tuning, and deployment.
• Strong proficiency in Python and familiarity with ML frameworks (e.g., PyTorch , TensorFlow, Hugging Face Transformers).
• Solid understanding and hands-on experience with core NLP techniques and architectures, especially Transformers.
• Experience with cloud platforms, particularly Google Cloud Platform (GCP), including services like Vertex AI, Cloud Storage, and compute services.
• Familiarity with MLOps principles and tools for model serving, monitoring, and pipeline automation.
• Excellent problem-solving skills, attention to detail, and ability to work independently and collaboratively.
• Active use of artificial intelligence (AI) tools and techniques to enhance performance, drive innovation, and improve decision-making across business functions.
• Ability to leverage AI tools and platforms to streamline workflows, improve decision-making, and drive innovation.
• Curiosity and adaptability in exploring emerging AI technologies, with a mindset for continuous learning and experimentation.

Requirements

• What Will Make You Stand Out (Preferred Qualifications):
• Hands-on experience building or contributing to agentic AI systems or multi-agent frameworks.
• Direct experience with document processing technologies such as OCR, layout parsing, Document AI, or custom information extraction from unstructured text.
• Experience with Vector Databases (e.g., pgvector , Pinecone, Weaviate , Qdrant ) and RAG architectures.
• Exposure to the healthcare domain, particularly understanding medical termino

Benefits

Additional Information

ABOUT THIS POSITION Job Description Summary We are seeking a highly skilled and innovative Machine Learning Engineer with a passion for building robust, efficient, and domain-specific AI systems using Language Models (LMs) and agentic architectures. As a core member of the team, you will be instrumental in developing the entire ML pipeline, from sophisticated data extraction techniques to fine-tuning specialized LMs and orchestrating their interactions within a multi-agent framework. This is a unique opportunity to apply state-of-the-art Generative AI and NLP techniques to a real-world, high-impact problem, leveraging the latest research in agentic AI and LMs to deliver economical and powerful solutions.

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Company Intel

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