As an AI Engineer, you will lead the design, development, and production deployment of AI-driven solutions that solve complex business problems at scale. This role goes beyond model training—you will take ownership of AI system architecture, influence technical direction, and ensure models are production-ready, scalable, and aligned with business objectives. You will play a key role in advancing the organization’s AI capabilities and mentoring other engineers while driving innovation across multiple AI domains.
Key Responsibilities
- Lead the design, development, and optimization of machine learning and deep learning models for real-world, production use cases.
- Own the end-to-end AI lifecycle, from problem definition and data exploration to model deployment, monitoring, and iteration.
- Architect and implement scalable, efficient data pipelines for training and inference on large and complex datasets.
- Evaluate model performance using robust metrics, conduct error analysis, and continuously improve model accuracy and reliability.
- Build and deploy neural networks using TensorFlow, PyTorch, or similar frameworks, ensuring production-grade quality.
- Collaborate closely with data engineers, software engineers, product managers, and domain experts to translate business needs into AI solutions.
- Drive technical decision-making around model selection, system architecture, and trade-offs (performance, cost, scalability).
- Ensure AI solutions follow ethical AI principles, data privacy standards, and regulatory requirements.
- Contribute to and review technical documentation, design proposals, and best practices for AI development.
- Mentor junior engineers and contribute to raising the overall technical bar of the AI team.
- Stay current with industry trends and research, and assess the adoption of new techniques or tools where they add real value.
- Support and improve model deployment, monitoring, and observability in production environments.
Requirements
Required Qualifications
- 2-3 years of hands-on experience in AI, machine learning, or related engineering roles.
- Strong proficiency in Python (Java or other languages is a plus).
- Deep experience with machine learning and deep learning frameworks such as TensorFlow or PyTorch.
- Proven experience working with large-scale datasets and building production-grade AI systems.
- Solid understanding of model evaluation, optimization, and performance trade-offs.
- Experience deploying AI models into production, with attention to scalability, reliability, and efficiency.
- Strong problem-solving skills and the ability to translate complex business challenges into AI-driven solutions.
- Excellent communication skills and experience working in cross-functional teams.
Nice to Have
- Experience with MLOps, model monitoring, and CI/CD for AI systems.
- Exposure to cloud platforms (AWS, GCP, Azure) for AI workloads.
- Experience in domains such as NLP, computer vision, recommendation systems, or predictive analytics.
- Prior experience mentoring or leading other engineers.