AI/ML Architect (offline)

As a Machine Learning Architect at GlobalLogic, you will be responsible for leading the design and implementation of our machine learning infrastructure and algorithms. Your work will directly contribute to the core of our product offerings, enhancing our capabilities in data analysis, predictive modeling, and AI-driven solutions. You will collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to ensure our ML systems are robust, scalable, and aligned with business goals.

 

Requirements:

Requried:

• 7+ years of experience in designing and implementing machine learning systems in a production environment.
• Profound knowledge of machine learning algorithms, including but not limited to supervised and unsupervised learning, deep learning, NLP,
• GenAI, and reinforcement learning.
• Experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Proficient in programming languages used in data science and ML, primarily Python and R.
• Strong understanding of data structures, algorithms, and software engineering principles.
• Experience with at least 2 cloud platforms (e.g., AWS, Azure, Google Cloud) and understanding of how to leverage their ML services.
• Knowledge of containerization and orchestration technologies (e.g., Docker, Kubernetes) for deploying ML models.
• Familiarity with MLOps principles and tools to streamline the ML lifecycle from development to production.
• Participate in pre-sales activities, including developing ML offering materials and engaging with clients to understand their needs, presenting
• tailored solutions, and demonstrating the potential impact of our ML technologies.
• Excellent communication and leadership skills, with the ability to work in a fast-paced, collaborative environment.

Preferred:

• Certifications in cloud technologies and machine learning.
• Experience with big data technologies (e.g., Hadoop, Spark).
• Published work in relevant fields.

Job Responsibilities:

• Design and implement scalable machine learning models and algorithms that can process large volumes of data efficiently.
• Lead the development of our ML infrastructure, ensuring it supports both current needs and future growth.
• Support presale PoCs and and project/account staring activities
• Architect and optimize data pipelines for training and deploying machine learning models.
• Collaborate with data engineers and software developers to integrate ML models into production systems.
• Stay abreast of new developments in machine learning, AI, and data science, and assess their applicability to our business needs.
• Provide technical leadership and mentorship to ML engineers and data scientists.
• Work with product teams to translate business requirements into technical specifications and ML solutions.