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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Scientist - GenAI / RAG - **Company:** AITA Consulting Services Inc. - **Location:** Dallas, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Graph Database, Monitoring of Systems, Python (Programming Language), Machine Learning, Tensorflow, Standard Sql, Feature Engineering, Pytorch, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Random Forest, Apache Spark, Deep Learning, Model Validation, Build Management, AI Platforms, Xgboost, Machine Learning Operations, Virtual Agents, Databricks - **Published:** September 18, 2026 - **Apply:** https://www.dice.com/job-detail/360f8ab9-a1f5-4519-8c89-9c3f026cf6cd ## About the Role Focus: We need candidates with real hands-on experience in Agentic AI and Traditional Data Science, not candidates who have only recently started exploring GenAI/LLMs. Continuous production implementation experience is preferred., * Candidates who can discuss end-to-end problem solving, feature engineering, predictive analytics, model selection, and solution design. * Hands-on experience designing and implementing RAG, LLM, and Agentic AI solutions. * Strong understanding of business use cases and ability to interact with customers and stakeholders. * Ability to explain architecture decisions, technical trade-offs, and implementation approaches. Mandatory Skills * Strong Data Science background * Strong Machine Learning implementation experience * Hands-on LLM & RAG architecture and implementation * Experience with Agentic AI concepts and workflows * Strong client-facing / consulting and stakeholder communication skills * Exposure to Deep Learning concepts and implementations * Experience with cloud AI platforms such as AWS Bedrock, Azure OpenAI, or Vertex AI, * Strong hands-on experience in Agentic AI and Multi-Agent Systems * Experience with LangGraph, LangChain, MCP (Model Context Protocol), tool calling, agent orchestration * Strong RAG implementation experience including Vector Databases, Hybrid Retrieval, Reranking, Knowledge Graphs * Hands-on experience with AWS Bedrock and enterprise GenAI solutions * Strong Python and SQL skills * Solid Traditional Data Science / Machine Learning background: + Classification + Regression + Forecasting + Anomaly Detection + Feature Engineering + Model Evaluation * Experience with XGBoost, CatBoost, Random Forest, Deep Learning frameworks (PyTorch/TensorFlow) * Experience designing and deploying production AI/ML systems * Understanding of MLOps / LLMOps, model monitoring, evaluation, observability, and retraining pipelines * Ability to translate business problems into ML or Agentic AI solutions * Experience with LLM Evaluation, Hallucination Detection, Groundedness and Retrieval Quality metrics * Exposure to Databricks, Spark, Vector Databases, APIs, Cloud Platforms (AWS preferred) ## Description * Build and deploy RAG (Retrieval-Augmented Generation) systems & AI chat interfaces * Work closely with client data science teams (ML/DL ecosystems) * Develop GenAI-based enterprise knowledge solutions * Collaborate directly with stakeholders and customers ## Related Videos - [RAG's Not Dead, You're Just Using It Wrong! - Phil Nash](https://www.wearedevelopers.com/videos/1906-rag-s-not-dead-you-re-just-using-it-wrong-phil-nash) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [TikTok's Privacy Innovation](https://www.wearedevelopers.com/videos/1036-tiktok-s-privacy-innovation) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment](https://www.wearedevelopers.com/videos/1152-ai-model-management-life-circles-ml-ops-for-generative-ai-models-from-research-to-deployment) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care)