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