Machine Learning Engineer

About The Company Our client is a consulting firm that gives its people a platform to shape the future of their clients’ businesses. They look for people who think differently, build boldly, and lead with impact, and they value individual perspective and ambition.

• Growth through challenge: The firm holds itself to high standards of excellence and accountability. You’ll work alongside elite consultants who push you to expand your skills and think more creatively, and you’ll own your work and its impact.
• Autonomy to do your best work: The firm cares about outcomes more than outputs. You’ll have room to shape your work, drive decisions, and anticipate client needs, in a culture that rewards bold thinking and challenges conventional wisdom.
• Diverse perspectives driving innovation: The firm champions inclusivity and authenticity and believes diverse perspectives lead to better solutions. The culture is energetic and collaborative, and the team believes great work should be fun.

What You’ll Do

• Design scalable machine learning infrastructure, including CI/CD pipelines for model training and deployment
• Monitor models in production
• Develop Generative AI solutions, including LLM-based applications, RAG infrastructure, and common search paradigms
• Ensure security and compliance
• Preprocess and analyze datasets
• Develop and fine-tune ML models to optimize performance
• Collaborate with client stakeholders to define and build robust AI solutions

What You’ll Bring

• Strong software engineering principles
• Experience designing and building cloud-native AI solutions (AWS, Azure, GCP)
• Familiarity with CI/CD and DevOps
• Expertise in data preprocessing and ML techniques
• Familiarity with model evaluation and libraries like TensorFlow and PyTorch
• Proven ML engineering experience, including model hosting, inference optimization, and model registry management
• Strong programming skills (Python, R)
• Experience with infrastructure as code and cloud services (networking, IAM, compute platforms, databases)
• Familiarity with containerization and orchestration tools (Docker, Kubernetes)
• Understanding of Agile methodologies