Overview:
Prodapt is the largest and fastest-growing specialized player in the Connectedness industry, recognized by Gartner as a Large, Telecom-Native, Regional IT Service Provider across North America, Europe and Latin America. With its singular focus on the domain, Prodapt has built deep expertise in the most transformative technologies that connect our world. Prodapt is a trusted partner for enterprises across all layers of the Connectedness vertical. Prodapt designs, configures, and operates solutions across their digital landscape, network infrastructure, and business operations – and craft experiences that delight their customers. Today, Prodapt’s clients connect 1.1 billion people and 5.4 billion devices, and are among the largest telecom, media, and internet firms in the world. Prodapt works with Google, Amazon, Verizon, Vodafone, Liberty Global, Liberty Latin America, Claro, Lumen, Windstream, Rogers, Telus, KPN, Virgin Media, British Telecom, Deutsche Telekom, Adtran, Samsung, and many more. A “Great Place To Work® Certified™” company, Prodapt employs over 6,000 technology and domain experts in 30+ countries across North America, Latin America, Europe, Africa, and Asia. Prodapt is part of the 130-year-old business conglomerate The Jhaver Group, which employs over 30,000 people across 80+ locations globally.
We are seeking an experienced Senior Generative AI / Machine Learning Engineer with 10+ years of experience to design, develop, and deploy enterprise AI solutions for one of our clients remotely. The ideal candidate will have strong expertise in Generative AI, Machine Learning, Large Language Models (LLMs), cloud platforms, and MLOps, with experience delivering scalable, production-ready AI solutions in consulting or managed services environments.
Responsibilities:
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Design, develop, and deploy enterprise-grade Generative AI and Machine Learning solutions for client engagements.
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Build AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and prompt engineering techniques.
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Collaborate with client stakeholders to understand business requirements and translate them into scalable AI solutions.
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Develop, fine-tune, evaluate, and optimize foundation models for enterprise use cases.
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Design scalable data pipelines and end-to-end ML workflows for model training, deployment, monitoring, and lifecycle management.
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Implement MLOps best practices, including CI/CD, model versioning, monitoring, governance, and automated retraining.
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Integrate AI solutions with enterprise applications, APIs, and cloud-native services.
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Ensure AI solutions meet security, compliance, performance, and reliability standards.
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Provide technical leadership, mentor engineering teams, and contribute to solution architecture and pre-sales activities.
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Support client implementations, troubleshooting, and continuous optimization of deployed AI solutions.
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Stay current with emerging AI technologies, frameworks, and industry best practices.
Requirements:
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Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Computer Engineering, Mathematics, Statistics, or a related technical field. Master's degree is preferred.
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10+ years of experience in Machine Learning, Artificial Intelligence, Data Science, or software engineering.
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1+ years of hands-on experience in Generative AI and Large Language Model (LLM) application development.
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Strong programming expertise in Python and experience with REST APIs.
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Hands-on experience with TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers, and related ML frameworks.
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Experience working with LLMs such as OpenAI GPT, Llama, Claude, Gemini, or similar foundation models.
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Strong expertise in RAG, prompt engineering, AI Agents, function calling, and LLM evaluation.
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Experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar AI orchestration frameworks.
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Experience with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or FAISS.
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Strong knowledge of MLOps using MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or similar platforms.
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Experience with AWS, Microsoft Azure, or Google Cloud Platform.
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Experience with Docker, Kubernetes, Git, CI/CD pipelines, and containerized deployments.
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Strong understanding of SQL, NoSQL databases, distributed data processing, and microservices architecture.
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Experience integrating AI solutions with enterprise platforms and business applications.
- Strong communication, client engagement, presentation, and stakeholder management skills.
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Experience working in Agile/Scrum delivery environments.
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Experience in consulting, managed services, or client-facing enterprise delivery.
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Knowledge of Responsible AI, AI governance, model security, and compliance frameworks.
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Experience with Azure OpenAI Service, Amazon Bedrock, Google Vertex AI, or similar enterprise AI platforms.
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Familiarity with multi-agent AI systems, multimodal AI, and AI workflow automation.
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Relevant cloud or AI certifications are a plus.
