AI/ML Engineer
US client via staffing partner · Remote
- Location
- Remote · Remote
- Salary band
- Band not stated
- Type
- Contract
- Level
- Mid
- Work authorization
- Not stated
Stack
Generative AI · LLM fine-tuning · RAG architecture · Vector Databases · Embeddings · LangGraph · CrewAI · Google ADK · PyTorch · OpenAI API · NLP · Python · SQL · FastAPI · REST APIs · Flask · Streamlit · Docker · Azure · AWS
About the role
Role: AI/ML Engineer Position: Contract Location: Remote Role Overview We are seeking an experienced AI/ML Engineer to design, build, and deploy enterprise-grade Generative AI and Machine Learning solutions. In this role, you will leverage LLMs, RAG architectures, agentic frameworks, and state-of-the-art ML techniques to solve complex problems and deliver high-impact applications. Key Responsibilities - Generative AI & LLM Solutions: Design, fine-tune, and deploy Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) architectures for real-world applications. - Agentic Workflows: Implement enterprise-level Agentic AI solutions utilizing modern open frameworks (e.g., LangGraph, CrewAI, Google ADK). - Document AI & Extraction: Develop intelligent document processing, OCR, and structured output creation pipelines. - Machine Learning & Deep Learning: Apply supervised, unsupervised, reinforcement learning, and NLP techniques to build robust predictive and analytical models. - Deployment & Integration: Build scalable REST/FastAPIs, containerize applications with Docker, and integrate lightweight UI interfaces (e.g., Streamlit, Flask) for demo and production setups. - Problem Solving & Innovation: Analyze complex data problems, perform root-cause analysis, stay updated with recent NLP research/literature, and deliver actionable technical solutions. Required Skills & Qualifications - Core AI/GenAI Expertise: Hands-on experience with GenAI implementation, LLM fine-tuning, RAG architecture, and Vector Databases / Embeddings. - Agentic Frameworks: Proficiency with frameworks like LangGraph, CrewAI, Google ADK, or similar. - Frameworks & Libraries: Strong proficiency in PyTorch, OpenAI API, and mainstream NLP/ML libraries. - Programming & Data: Advanced Python and SQL skills, with a track record of data analysis and modeling (3+ years required). - Application & API Development: Hands-on experience with FastAPI, REST APIs, Flask, Streamlit, and Docker containerization. - Cloud Platforms: Experience developing and deploying AI solutions in Azure, AWS, or GCP environments. - Models & Research: Broad knowledge of both open-source and proprietary (OpenAI) LLM models, along with familiarity with current NLP literature and conference venues.
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