> Markdown version of [/jobs/ext/2737740-senior-ai-engineer](https://www.wearedevelopers.com/jobs/ext/2737740-senior-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior AI Engineer - **Company:** NEXT TV INC - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Data Analysis, Computer Vision, Cloud Engineering, Cluster Analysis, Encodings, Computer Programming, Databases, Continuous Integration, Information Engineering, Data Transformation, Database Queries, Decision Support Systems, Linux, Python (Programming Language), Knowledge-Based Systems, Machine Learning, NumPy, Object Detection, Pattern Recognition, Performance Tuning, Recommender Systems, Tensorflow, Standard Sql, Search Technologies, Video Editing, Visual Analytics, Workflow Management Systems, Enterprise Software Applications, Feature Engineering, Chatbots, Pytorch, ReactJS, Large Language Models, Multi-Agent Systems, Prompt Engineering, Deep Learning, Model Validation, Generative AI, Pandas, Containerization, AI Platforms, Pyspark, Scikit Learn, Kubernetes, Performance Monitor, Apache Kafka, Machine Learning Operations, Feature Extraction, Virtual Agents, Software Version Control, Automation Anywhere, Docker - **Published:** September 5, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pho4vj5q9u ## About the Role * 5+ years of experience in Machine Learning Engineering, Data Science, AI Engineering, or Applied AI development. * Strong programming expertise in Python and modern AI development frameworks. * Solid understanding of machine learning fundamentals including supervised, unsupervised, and probabilistic learning techniques. * Hands-on experience with Scikit-learn, PySpark, Pandas, NumPy, Airflow, Kafka, and related data engineering libraries. * Experience building and deploying machine learning models in enterprise environments. * Strong expertise in feature engineering, exploratory data analysis, model validation, and performance optimization. * Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow. * Good understanding of computer vision concepts including image classification, object detection, feature extraction, and visual analytics. * Experience developing Generative AI solutions using LLMs, embeddings, vector databases, and prompt engineering techniques. * Hands-on exposure to Retrieval-Augmented Generation (RAG), semantic search, and enterprise knowledge systems. * Experience designing and implementing AI agents, multi-agent systems, and workflow automation frameworks. * Strong understanding of advanced reasoning methodologies including ReAct, Chain-of-Thought (CoT), Tree-of-Thought (ToT), reflection-based prompting, and structured reasoning. * Experience with LangGraph, orchestration frameworks, or agent workflow design is preferred. * Knowledge of SQL, data modeling, and data querying techniques. * Experience working in Linux or WSL environments. * Understanding of MLOps practices including model deployment, monitoring, drift detection, CI/CD, version control, and automation. * Exposure to Docker, MLflow, cloud-native AI platforms, or containerized deployments is an added advantage. * Experience working with predictive maintenance, industrial IoT, manufacturing analytics, quality inspection, or operational intelligence solutions is preferred. * Knowledge of financial analytics, cost optimization, performance benchmarking, or business intelligence use cases is desirable. * Strong analytical thinking, structured problem-solving, and critical reasoning capabilities. * Excellent communication, collaboration, and stakeholder engagement skills. * Ability to operate effectively in fast-paced, innovation-driven, and evolving AI environments. * Passion for emerging AI technologies, continuous learning, and building impactful enterprise solutions. ## Description * You will join our high-performance Data & AI team and contribute to building intelligent AI, Machine Learning, Computer Vision, Generative AI, and Agentic AI solutions that drive measurable business outcomes across enterprise transformation initiatives. * Develop, train, and optimize machine learning models for classification, regression, forecasting, clustering, anomaly detection, recommendation systems, and predictive analytics use cases. * Work with time-series, multivariate sensor data, and operational datasets to build predictive maintenance, machine health monitoring, and operational intelligence solutions. * Design and implement scalable feature engineering pipelines, including statistical, rolling-window, and domain-specific transformations. * Apply advanced statistical and probabilistic techniques including Bayesian inference, uncertainty estimation, and simulation-based modeling where appropriate. * Build and fine-tune deep learning models using PyTorch or TensorFlow for enterprise AI applications. * Develop computer vision solutions for visual inspection, defect detection, image classification, object detection, and pattern recognition use cases. * Design and manage image and video processing pipelines, including preprocessing, augmentation, labeling, and model evaluation workflows. * Develop Generative AI solutions leveraging Large Language Models (LLMs) for document summarization, conversational AI, knowledge retrieval, and insight generation. * Implement Retrieval-Augmented Generation (RAG) architectures, embedding-based semantic search, and enterprise knowledge retrieval solutions. * Design, develop, and deploy AI agents capable of tool usage, multi-step reasoning, workflow execution, and intelligent decision support. * Implement advanced reasoning techniques including Chain-of-Thought (CoT), Self-Consistency, Reflection, ReAct, and Structured Reasoning patterns to improve AI reliability and performance. * Develop tool-augmented AI workflows that integrate reasoning, validation, execution, and iterative optimization. * Design memory-augmented AI systems utilizing vector databases, episodic memory, semantic memory, and hierarchical retrieval strategies. * Implement agent evaluation frameworks including reasoning trace analysis, tool usage effectiveness, response quality scoring, and performance monitoring. * Monitor AI systems for cost optimization, latency management, token usage, model quality, and operational efficiency. * Build reusable data engineering pipelines for ingestion, cleansing, transformation, and processing of data from enterprise systems, APIs, databases, image streams, and industrial environments. * Develop and maintain automated workflows for model training, deployment, monitoring, and retraining using modern MLOps practices. * Design multi-agent systems with planner-executor-validator architectures and human-in-the-loop controls. * Leverage LangGraph and orchestration frameworks to build scalable agent workflows supporting branching logic, retries, state management, and conditional execution. * Support financial, operational, and business analytics initiatives through AI-powered insights, decision support, and scenario modeling solutions. * Collaborate with architects, product teams, data engineers, business stakeholders, and domain experts to translate business challenges into scalable AI solutions. * Document models, architectures, assumptions, APIs, workflows, and best practices to ensure maintainability and knowledge sharing. ## Related Videos - [Vectorize all the things! Using linear algebra and NumPy to make your Python code lightning fast.](https://www.wearedevelopers.com/videos/562-vectorize-all-the-things-using-linear-algebra-and-numpy-to-make-your-python-code-lightning-fast) - [Docker network without Docker](https://www.wearedevelopers.com/videos/1418-docker-network-without-docker) - [Advanced Typing in TypeScript](https://www.wearedevelopers.com/videos/496-advanced-typing-in-typescript) - [AI in Production: applied AI & enterprise use cases](https://www.wearedevelopers.com/videos/100130-ai-in-production-applied-ai-enterprise-use-cases) - [How to implement convenient Python bindings to C++](https://www.wearedevelopers.com/videos/618-how-to-implement-convenient-python-bindings-to-c) - [Navigating the AI Revolution in Software Development](https://www.wearedevelopers.com/videos/1266-navigating-the-ai-revolution-in-software-development) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [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) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers)