AI/ML Engineer

AI/ML Engineer 1 year + 4 days onsite NYC Job Summary We are seeking a highly skilled Senior Developer to lead the development of a Python-based platform that ingests internal data sources (e.g., Hadoop, REST APIs) and applies locally deployed language models (LLMs) for text analysis, including issue classification and summarization. This role requires a blend of strong engineering expertise in building scalable data systems and good communication skills. Key Responsibilities Architect and implement a robust data ingestion pipeline using Python. Integrate with Hadoop and/or internal APIs for sourcing structured and unstructured data. Design modular components for data transformation, enrichment, and routing to downstream NLP models. LLM Integration Incorporate local LLM models for classification and summarization tasks. Use prompt orchestration, chaining, and context-aware techniques to improve NLP accuracy and consistency. Ensure performance and stability of LLM-based components in production environments. Collaboration & Engineering Practices Work closely with data engineers, product owners, and ML researchers to refine use cases and deliver high-quality solutions. Follow modern software engineering best practices including testing, CI/CD, and code documentation. Participate in design reviews and knowledge-sharing sessions. Required Qualifications 4+ years of professional experience in Python software development. Proven experience working with big data systems, particularly Hadoop, PySpark, or related technologies. Practical experience using LLMs, vector databases, embedding pipelines, and retrieval-augmented generation (RAG) architectures. Familiarity with NLP tasks such as classification, summarization, and information extraction. Experience building or maintaining APIs and microservices. Experience with Model Context Protocol (MCP) for managing prompts and contextual data across LLM applications. Preferred Qualifications Familiarity with LLMOps tools and scalable inference strategies. Prior work with LangChain, Hugging Face Transformers, or vLLM runtime environments. Data scientist–related experience, such as collaborating on model training and evaluation, aligning data processing logic to ML objectives, or working on feature engineering and experimentation pipelines. Background in financial services, enterprise software, or regulated environments. RIT Solutions