Sr Engineer, Machine Learning Engineering

Overview

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The Senior Engineer, Machine Learning plays a pivotal role in advancing AI capabilities, focusing on the design, development, and deployment of large language models (LLMs) and generative AI solutions. This position is essential for building scalable, production-grade AI systems that enable automation, personalization, and intelligent decision-making across the enterprise. The role emphasizes the creation of innovative GenAI applications that deliver real-world business impact while maintaining high standards of performance, reliability, and responsible AI practices.

Collaborating with cross-functional technical teams, they ensure the seamless integration of LLM-powered solutions into products and workflows, reinforcing the organization’s leadership in applying advanced AI technologies.

• Build and manage the complete machine learning and generative AI lifecycle, including research, design, experimentation, development, deployment, monitoring, and maintenance.

• Design, develop, and deploy LLM-based and generative AI models to power scalable and intelligent enterprise applications.

• Architect, optimize, and maintain retrieval-augmented generation (RAG), prompt orchestration, and contextual reasoning pipelines to support diverse AI use cases.

• Implement scalable MLOps pipelines for model deployment, performance monitoring, and continuous improvement.

• Conduct fine-tuning, alignment, and evaluation of LLMs and multimodal models to ensure reliability, efficiency, and fairness.

• Collaborate with data science, engineering, and product teams to translate business needs into generative AI-driven solutions.

• Perform benchmarking, evaluation, and optimization of generative models to improve accuracy, latency, and cost efficiency.

• Research and apply emerging techniques in transformer architectures, multimodal learning, and generative modeling to drive innovation and enhance enterprise capabilities.

• Ensure secure, ethical, and responsible AI deployment, embedding fairness, transparency, and compliance throughout the model lifecycle.

• Mentor and guide team members on generative AI frameworks, best practices, and experimentation methodologies.

• Participate in other duties or projects as assigned by business management as needed.

• Bachelor's Degree Computer Science, Data Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Required)

• Master's/Advanced Degree Computer Science, Data Science, Statistics, Informatics, Information Systems, Machine Learning, or another quantitative field (Preferred)

• 1+ year of experience in designing, developing, and deploying large language models (LLMs) and generative AI systems in production environments (Required)

• 5+ years of experience building and maintaining end-to-end ML pipelines, including data ingestion, training, deployment, monitoring, and optimization (Required)

• 3+ years of experience applying MLOps practices and leveraging cloud platforms (AWS, GCP, or Azure) for scalable AI solutions (Required)

• Experience implementing fine-tuning, evaluation, and benchmarking techniques for LLMs and generative AI applications (Preferred)

• 5+ years of experience collaborating with cross-functional teams (engineering, data science, and product) to deliver AI-powered applications (Required)

• 2+ years of experience in programming languages such as Python/R, Java/Scala, and/or Go, with hands-on experience in frameworks such as PyTorch, Tensor Flow, Lang Chain, or Hugging Face (Required)

• Experience in the telecom or large-scale enterprise domain (Preferred)

• 5+ years in designing, building, and deploying machine learning and generative AI models…