Job Summary

We are looking for a skilled AI Engineer to design, build, deploy, and support scalable AI solutions that solve real business problems. This role combines machine learning, generative AI, software engineering, and cloud engineering to deliver production-grade AI applications in a secure and governed enterprise environment. The AI Engineer will work closely with cross-functional teams to translate use cases into reliable, high-quality solutions and continuously improve AI capabilities across the organization.

Key Responsibilities

• Design, develop, test, and deploy AI/ML and generative AI solutions for business use cases.
• Build and maintain scalable data, training, and inference pipelines to support production AI workloads.
• Develop and optimize machine learning models, LLM-based applications, and AI services for performance, accuracy, scalability, and reliability.
• Implement MLOps and LLMOps practices including versioning, testing, monitoring, deployment automation, and performance evaluation.
• Design and build AI applications using techniques such as prompt engineering, retrieval-augmented generation (RAG), embeddings, and orchestration frameworks where relevant.
• Integrate AI solutions into enterprise platforms, applications, and sandbox environments in collaboration with product, platform, and engineering teams.
• Monitor deployed AI systems, troubleshoot production issues, and continuously improve solution performance and user experience.
• Ensure AI solutions comply with data protection, model governance, security, and technology risk management requirements.
• Stay current with emerging AI technologies, tools, and best practices, and apply them pragmatically to enterprise use cases.
• Collaborate with business, technology, and external partners to translate requirements into robust and maintainable AI solutions.

Skill Requirements

Required Qualifications/Skills:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.
• Proven experience in designing, developing, and deploying AI/ML solutions in production environments.
• Strong programming skills in Python; knowledge of R, Java, or similar languages is an advantage.
• Hands-on experience with machine learning and deep learning frameworks such as TensorFlow, PyTorch, and scikit-learn.
• Experience with large language models, NLP, computer vision, or other relevant AI domains.
• Good understanding of data preprocessing, feature engineering, model evaluation, and experiment tracking.
• Practical knowledge of MLOps/LLMOps concepts including model lifecycle management, deployment pipelines, monitoring, and governance.
• Experience with cloud platforms and services such as Microsoft Azure, AWS, or Google Cloud.
• Strong analytical, problem-solving, and troubleshooting skills with attention to quality and detail.
• Excellent communication and collaboration skills, with the ability to work effectively across business and technical teams.

Preferred Skills:

• Experience in building GenAI solutions using prompt engineering, RAG, embeddings, vector databases, and orchestration frameworks.
• Experience with API development, containerization, CI/CD, and software engineering best practices for production deployment.
• Familiarity with AI governance, data privacy, security controls, and responsible AI practices in enterprise environments.
• Experience working in regulated industries such as financial services or insurance.
• Passion for innovation, continuous learning, and applying AI to create meaningful business impact.

Other Requirements

1. Optional But Valuable Certifications: Certified Data Scientist (Cds), Tensorflow Developer Certificate, Or Professional Data Engineer Certification.