Senior AI/ML engineer

Prodapt•Richardson, TX

About The Position

Prodapt is the largest specialized player in the Connectedness industry. As an AI-first strategic technology partner, Prodapt provides consulting, business reengineering, and managed services for the largest telecom and tech enterprises building networks and digital experiences of tomorrow. A ServiceNow-invested company, Prodapt has been recognized by Gartner as a Large, Telecom-Native, Regional IT Service Provider. A “Great Place To Work® Certified™” company, Prodapt employs over 6,000 technology and domain experts across the Americas, Europe, India, Africa, & Japan. Prodapt is part of the 130-year-old business conglomerate The Jhaver Group, which employs over 32,000 people across 80+ locations globally. We are looking for a skilled AI ML Engineer to join our AI Data & Practice team.

Requirements

12+ years of experience in software engineering and enterprise technology solutions.
Required telecom domain experience with strong knowledge of telecommunications systems, OSS/BSS platforms, network operations, customer experience platforms, and telecom data ecosystems, including experience applying AI/ML solutions in telecom environments.
Extensive hands-on experience designing and developing Agentic AI solutions using frameworks such as LangGraph, MCP, LangChain, and enterprise AI agent frameworks to build autonomous, goal-oriented systems.
Strong expertise in Generative AI, LLMs, SLMs, Retrieval-Augmented Generation (RAG), prompt engineering, fine-tuning, vector embeddings, vector databases, semantic search, and document intelligence solutions.
Advanced programming skills in Python with experience developing scalable AI/ML applications using frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, Scikit-learn, and related AI/ML technologies.
Experience designing and implementing Knowledge Graph solutions, cloud-based AI platforms (AWS/Azure/GCP), CI/CD pipelines, containerization technologies (Docker/Kubernetes), automated testing, and production deployment workflows.
Proven ability to provide technical leadership, collaborate with architects and business stakeholders, translate complex telecom requirements into AI solutions, and drive Agile software development practices across cross-functional engineering teams.
Bachelor’s degree in Computer Science, Electrical Engineering, Computer Engineering, or a related field.

Responsibilities

Design and develop Agentic AI solutions using frameworks such as LangGraph Studio, MCP, and the VZ Agentic framework (PyVegas, VEGAS, DPF) to build autonomous, goal-driven AI systems.
Develop and optimize vector embedding pipelines, advanced vector search, and intelligent document chunking strategies to improve semantic search and retrieval accuracy.
Design and implement Knowledge Graph-based solutions to enhance contextual understanding, reasoning, and AI-driven decision-making.
Develop, fine-tune, and optimize Large Language Models (LLMs) and Small Language Models (SLMs) for domain-specific use cases.
Build advanced prompt engineering workflows and optimize token utilization to improve model performance, scalability, and cost efficiency.
Develop clean, scalable, and production-ready Python applications using modern AI/ML frameworks and best coding practices.
Collaborate with business stakeholders, solution architects, and technical leads to translate business requirements into scalable AI/ML solutions and technical designs.
Provide technical guidance on AI model behavior, data dependencies, model selection, and implementation strategies throughout the software development lifecycle.
Design, implement, and maintain CI/CD pipelines for AI/ML model deployment, versioning, and continuous integration across development and staging environments.
Develop comprehensive unit tests, perform model validation, and execute end-to-end testing to ensure solution quality, reliability, and compliance with engineering standards.
Monitor model performance, conduct code quality reviews, and support production deployments by troubleshooting issues and ensuring post-release stability.
Participate in Agile ceremonies, including sprint planning, daily stand-ups, backlog refinement, demos, and retrospectives, while collaborating effectively with cross-functional engineering teams.