Senior AI Engineer

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Company Overview

A leading AI and data science consulting organization specializes in helping enterprises solve complex business challenges through Artificial Intelligence, machine learning, cloud technologies, and advanced analytics. The organization partners with clients across both private and public sectors, delivering innovative solutions in areas such as healthcare, life sciences, autonomous systems, renewable energy, and digital transformation. Their multidisciplinary teams combine expertise in AI strategy, data science, engineering, and cloud infrastructure to drive measurable business outcomes.

The organization is seeking a Senior AI Engineer to join its growing AI engineering team. This role offers the opportunity to work alongside data scientists, machine learning engineers, project managers, and industry specialists to design, build, and deploy robust AI infrastructure and machine learning solutions for enterprise clients.

The successful candidate will combine expertise in data engineering, cloud architecture, MLOps, and AI deployment while contributing to cutting-edge projects across multiple industries. This position also provides opportunities to develop technical leadership skills, mentor team members, and influence the future of AI-powered business transformation.

Key Responsibilities

• Collaborate with clients to design, develop, and deploy architectures that support machine learning and automation applications.
• Leverage expertise in modern data architecture, data science engineering, data transformation, and management of structured and unstructured data.
• Design and lead the development of scalable, high-performance data architecture solutions that support business operations and AI/Generative AI use cases.
• Support and enhance data architectures, data pipelines, and database schemas across graph, relational, and NoSQL databases.
• Participate in architecture and deployment discussions to ensure solutions are scalable, secure, highly available, and production-ready.
• Implement best practices in automation, high-performance computing (HPC), AI infrastructure, and engineering design patterns.
• Lead proof-of-concept initiatives to validate emerging data, cloud, and AI technologies.
• Drive modernization efforts through thought leadership and execution of modern data architecture principles.
• Mentor and coach junior team members on technical best practices and professional development.
• Work closely with cross-functional teams to deliver innovative solutions for complex business and technical challenges.

Required Qualifications

• Bachelor’s degree in a STEM field such as Computer Science, Engineering, Physics, or equivalent practical experience.
• 4+ years of experience in data engineering, data science, software engineering, MLOps, or AI/ML deployment.
• 4+ years of experience designing and supporting cloud-based production solutions using AWS, Azure, Google Cloud, or equivalent platforms.
• 4+ years of programming experience with technologies such as Python, SQL, Linux Shell/CLI, and PowerShell.
• 2+ years of experience leading technical teams and delivering complex, mission-critical projects.
• 2+ years of experience with DevOps and CI/CD tools such as Puppet, Ansible, Chef, Airflow, Terraform, and Jenkins.
• 2+ years of experience with database development and ETL/ELT pipelines, including relational, NoSQL, and graph databases.
• 2+ years of experience with deployment and optimization technologies including Kubernetes, Docker, Kubeflow, MLflow, Kafka, NVIDIA TensorRT/Triton, RAPIDS, or related platforms.
• Ability to work independently and collaboratively in a fast-paced environment.
• Strong written and verbal communication skills.
• Excellent project management, prioritization, and stakeholder management abilities.
• Willingness to travel approximately 10% as required.

Preferred Qualifications

• Master’s degree in Computer Science, Engineering, Physics, or a related STEM discipline.
• AWS or Azure certifications, including Solutions Architect, DevOps Engineer, or SysOps Administrator credentials.
• Experience with GPU computing technologies such as CUDA, OpenCL, and high-performance computing (HPC) environments.
• Experience working with large-scale AI, machine learning, and cloud-native infrastructure deployments.

Disclaimer

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