Engineering Manager, AI Solutions

As the Engineering manager, you will be managing the roadmap priorities for AI/ML Solution and will be point of contact for all technical issues and decisions, while managing stakeholders and partners across the organization.

• Provide technical leadership manage a high-performing engineering team to build and scale our next generation AI Solutions .
• Hire and directly lead a diverse team of top-tier ML and software engineers, taking charge of all facets of AI Products
• Design, Build, optimize , fine-tune Generative AI/LLM models/AI Solutions that can be integrated with numerous applications, support thousands of tenants, and operate at scale in production to support various Paypal AI Personalization initiatives
• Ensure high code quality, performance, and reliability through rigorous testing, code reviews, and adherence to software development best practices.
• Drive innovation by researching and incorporating state-of-the-art machine learning techniques, tools, and frameworks into the platform.
• Effective communication, listening, interpersonal, influencing, and alignment driving skills; able to convey important messages in a clear and compelling manner
• Collaborate with cross-functional teams including product managers, data scientists, and software engineers and stakeholders to define platform requirements and priorities.
• Develop and evolve engineering processes and collaboration models to optimize team efficiency and collaboration
• Mentor team members, provide technical guidance, and foster a culture of collaboration, innovation, and continuous learning.
• Stay up to date with the latest advancements in AI/ML technology and industry trends and leverage this knowledge to enhance the platforms capabilities

What Do You Need to Bring

Qualifications

• Proven experience in leading or managing machine learning/AI teams, with a track record of building successful AI solutions and productizing ML Models, Features Stores
• Solid track record of over-achieving engineering and platform delivery and scaling targets in high volume, innovative and fast-paced high-pressure environment; proven results in delivery on platform products.
• Masters / bachelor s in computer science, Computer engineering, Machine Learning, Data Mining, Information Systems, or related disciplines, with technical expertise in one or more of the above-mentioned areas or equivalent practical experience.
• Experience developing Gen AI applications/ services using Prompt Engineering, LLMs, and fine-tuning methods for sophisticated business use cases with large amounts of unstructured data.
• Strong background in deep learning techniques, particularly in NLP and Vision
• Proficiency in multiple Programming/scripting languages, i.e. Python, Java, Scala, SQL, NoSQL (like HBase, Redis, Aerospike)
• Strong proficiency in machine learning concepts, algorithms, and techniques, with hands-on experience in developing and deploying machine learning models.
• Expertise in building ML pipelines with Big Data technologies such as Hadoop, Spark, HBase, Kafka.
• Good understanding of distributed systems, data streaming, complex event Processing, NoSQL solutions for creating and managing data integration pipelines for batch and Real Time data needs.
• Experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, etc.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes). Experience in Azure is a plus
• Strong communication , listening, interpersonal, influencing, and alignment driving skills; able to convey important messages in a clear and compelling manner
• Demonstrated leadership abilities, including the ability to inspire, mentor, and empower team members to achieve their full potential.

Preferred

• Prior experience in Content Understanding, enrichment, entity resolution or knowledge graph
• Strong background in MLOps and experimentation frameworks
• Extensive experience with concurrent, parallel, and distributed computing, including performance tuning and optimization for large-scale applications