Dice is the leading career destination for tech experts at every stage of their careers. Our client, Business Integra, is seeking the following. Apply via Dice today! Job Title: Python Developer (AWS + Glue) Location: Jersey City, NJ (Onsite) Job Type: W2 Contract Who are we looking for? A highly skilled individual who can work efficiently using programming language Python, AWS Glue and Hands-on experience including databases (SQL/NoSQL/Graph), web frameworks, APIs with 6 + years of relevant experience. Technical Skills: Python, AWS Glue, Hands-on experience in applied AI/ML engineering, backend development - databases (SQL/NoSQL/Graph), web frameworks, APIs, and microservices. Process Skills: Core Skills : • 6+ years of engineering experience with strong programming skills in Python, AWS Glue with experience in developing and maintaining production-level code. • Good to have hands-on experience in applied AI/ML engineering with 2+ years, with a track record of developing and deploying business-critical machine learning models and applications in production.s • Proficient in programming languages like Python for model development and experimentation, and integration with GenAI platform. • Strong collaboration and communication skills to work effectively with geographically spread cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects. • Strong problem-solving and analytical skills with emphasize on attention to detail. • Experience with cloud platforms, for deploying and scaling AI/ML models. Desired skills but not mandatory: • Experience in backend development, including databases (SQL/NoSQL/Graph), programming languages (Python/Java/Node.js), web frameworks, APIs, and microservices and possess front-end development skills. • Knowledge of SRE practices. • Knowledge of large language models (LLMs) and accompanying toolsets the LLM ecosystem (e.g. Lang chain, Vector databases, opensource Models like Mistral, Llama, etc) • Exposure to cloud automation technologies such as Terraform • Assess and choose suitable LLM tools and models for diverse tasks including but not limited to curating custom datasets and fine-tune LLM with a focus on parameter-efficient, mixture-of-expert, and instruction methods designing and developing advanced LLM prompts, Retrieval-Augmented Generation (RAG) solutions, and Intelligent agents for the LLMs and executing experiments to push the capability limits of LLM models and enhance their dependability.