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Position Overview

As the Technical Lead – AI & Machine Learning at Arrow Electronics, you will play a key role in delivering enterprise AI and machine learning solutions across intelligent products and internal automation platforms. Working under the direction of the AI leadership team, you will translate strategic AI initiatives into scalable, production-ready solutions while providing technical leadership to a team of AI engineers and data scientists.

You will lead the implementation of advanced AI systems throughout the complete machine learning lifecycle, including data preparation, model development, experimentation, deployment, monitoring, and continuous optimization. Your expertise will span machine learning, deep learning, neural networks, generative AI, intelligent automation, and large language models.

As the technical lead for project execution, you will collaborate closely with software engineering, product management, and data engineering teams to ensure AI solutions are delivered with high quality, scalability, and business impact while mentoring engineers and promoting engineering best practices.

Key Responsibilities

Technical Execution

• Lead the technical implementation of AI and machine learning initiatives aligned with the organization's AI strategy and roadmap.
• Translate product and AI leadership vision into scalable technical solutions.
• Drive engineering best practices, code quality, documentation, and software design across AI projects.
• Collaborate with AI leadership, product managers, software engineers, and data engineers to successfully deliver AI-powered products.

Machine Learning & Deep Learning

• Design, develop, and optimize machine learning, deep learning, and neural network models for real-world business applications.
• Build predictive models, recommendation systems, forecasting models, anomaly detection solutions, classification systems, and intelligent automation workflows.
• Fine-tune transformer models and other deep learning architectures using modern training techniques.

Generative AI & Intelligent Systems

• Develop applications leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, semantic search, embeddings, and prompt engineering.
• Build intelligent workflows integrating LLMs with enterprise systems, vector databases, and external tools.
• Evaluate new AI technologies and recommend practical adoption opportunities.
• Deploy and maintain AI models in production environments.
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Production AI & MLOps

• Ensure AI systems are reliable, secure, efficient, and maintainable.

Mentorship & Innovation 

• Mentor AI engineers and junior machine learning engineers through technical guidance, code reviews, and knowledge sharing.
• Promote engineering excellence, continuous learning, and adoption of modern AI development practices.
• Support technical interviews and onboarding of new team members.
• Stay current with emerging developments in artificial intelligence, machine learning, deep learning, and generative AI.

Qualifications

• 6+ years of experience developing and deploying production AI and machine learning solutions.
• Demonstrated experience leading the technical implementation of AI/ML projects.
• Strong proficiency in Python and data science libraries including NumPy, pandas, scikit-learn, and related tools.
• Strong understanding of statistics, machine learning algorithms, feature engineering, and model evaluation.
• Strong knowledge of neural networks, transformer architectures, and modern deep learning techniques.
• Experience building applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and prompt engineering.
• Experience with NLP, document intelligence, semantic search, embeddings, information extraction, and named entity recognition.

Preferred Qualifications

• Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field is preferred.
• Experience deploying AI services using cloud platforms and modern software engineering practices.
• Familiarity with vector databases, search technologies, and MLOps practices.

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