AML Engineer - GCH

The Senior ML/AI Engineer will lead the design, development, and deployment of machine learning models and artificial intelligence solutions, focusing on solving complex business challenges through predictive analytics, natural language processing, and deep learning techniques. The role involves collaborating closely with data scientists, data engineers, and business stakeholders to create scalable, production-grade ML/AI models that align with the organization's strategic goals. Additionally, the Senior ML/AI Engineer will drive innovation by exploring new AI methodologies, including large language models, and integrating them into data solutions for enhanced customer engagement and business insights.

Machine Learning Model Development:

• Build, train, and deploy advanced machine learning models, including regression, classification, clustering, and recommendation algorithms, that deliver business value.
• Implement NLP, deep learning, and computer vision solutions as required to support customer-centric applications and predictive analytics.
• Apply knowledge of large language models (LLMs) to develop conversational AI and recommendation systems for customer engagement.

AI System Design & Deployment:

• Design end-to-end ML/AI pipelines that support the continuous integration and deployment of machine learning models into production environments.
• Leverage MLOps best practices for model versioning, retraining, performance monitoring, and scalability.
• Ensure models are optimized for latency, accuracy, and scalability by deploying on cloud platforms such as AWS, GCP, or Azure.

Data Gathering and Preprocessing:

• Collaborate with data engineering teams to design and optimize ETL/ELT pipelines for AI-specific data needs.
• Engineer and preprocess large, complex datasets from various sources to ensure model robustness, accuracy, and generalizability.
• Contribute to the centralized data knowledge management system to streamline data access for ML/AI use cases.

Predictive Analytics & Foresight Generation:

• Perform predictive analytics to support business strategies, creating foresight-driven models that enhance customer experiences and drive revenue growth.
• Evaluate and implement techniques for model interpretability, explainability, and bias reduction.

Collaboration & Stakeholder Engagement:

• Partner with business stakeholders to identify areas where ML/AI can drive value, and translate these into actionable AI projects.
• Work closely with data scientists, data engineers, and software development teams to ensure successful model deployment and alignment with data architecture.
• Document processes, code, and models for knowledge sharing and team scalability.

Continuous Improvement & Research:

• Stay updated on advancements in ML/AI, particularly LLMs and generative AI, and identify opportunities to apply these innovations to business problems.
• Conduct ongoing model evaluation and improvement based on performance metrics, customer feedback, and evolving business needs.

Education:

• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
• Master’s or Ph.D. in Machine Learning, AI, or a similar field is strongly preferred for a senior role.

Experience Requirements:

• 5+ years of hands-on experience in machine learning, AI engineering, or data science, with a track record of successfully deploying models in production.
• Extensive experience working with large datasets, building and fine-tuning ML models, and deploying on cloud platforms.
• Demonstrated expertise in deep learning frameworks (e.g., TensorFlow, PyTorch) and familiarity with NLP and large language models.

Preferred Technical Skills:

• Proficiency in Python and ML libraries (e.g., scikit-learn, Keras, Hugging Face).
• Strong experience in cloud-based ML services (e.g., AWS SageMaker, GCP AI Platform, Azure ML).
• Knowledge of MLOps tools and practices (e.g., MLflow, Airflow, Docker, Kubernetes).

Machine Learning and AI Certifications:

• AWS Certified Machine Learning – Specialty: Recognized for building, training, and deploying ML models on AWS.
• Google Professional Machine Learning Engineer: Certifies knowledge in deploying ML models on Google Cloud.
• Microsoft Certified: Azure AI Engineer Associate: A certification focusing on Azure-based AI development.
• Certified Machine Learning Professional (CMLP): Industry-recognized certification for ML proficiency.

Deep Learning and NLP Certifications:

• Deep Learning Specialization by Andrew Ng (Coursera): Covers foundational and advanced deep learning techniques.
• Natural Language Processing with Deep Learning (Stanford or equivalent): For proficiency in NLP techniques and applications.

Data Science and Analytics:

• Certified Data Scientist (CDS) by DASCA: A foundational certification for data science, often a complement to ML expertise.
• TensorFlow Developer Certification: Useful for engineers focusing on deep learning.

MLOps and Cloud Certifications:

• MLOps Certification by Google: Demonstrates proficiency in end-to-end ML pipeline management and deployment.
• Certified Kubernetes Administrator (CKA): Beneficial for deploying and managing containerized ML models.

Technical Proficiency and Model Engineering:

• Proficient in a range of ML/AI techniques, including supervised and unsupervised learning, NLP, deep learning, and computer vision.
• Advanced knowledge of large language models, with experience in implementing LLM-based solutions like conversational AI or text generation models.

Strategic and Analytical Thinking:

• Strong ability to identify business needs and translate them into AI-driven solutions, ensuring alignment with organizational goals.

Collaboration and Communication Skills:

• Effective at communicating technical information to non-technical stakeholders, ensuring alignment and buy-in for ML initiatives.
• Excellent collaboration skills to work with cross-functional teams and drive alignment on ML/AI deployment.

Data Preprocessing and Analysis:

• Skilled in data preprocessing and transformation, ensuring data readiness for robust and accurate ML modeling.
• Strong understanding of data architecture, including how data pipelines and ETL processes fit into the larger data ecosystem.

Problem Solving and Adaptability:

• Ability to troubleshoot complex ML/AI challenges and adapt to changes in technology or business requirements.
• Proactive approach in seeking out and testing new AI technologies and methodologies to stay competitive in the field.

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