Description
The AI Engineer position builds both AI solutions in the form of PoCs and fully production-ready products based on LLM platforms and traditional machine learning problems to support enterprise digital transformation. This role works closely with Product Owners and Solution Architects to translate requirements into practical, scalable, and secure AI applications.
- Develop PoCs and AI/ML solutions in the areas of LLMs and traditional machine learning, balancing experimentation and real-world deployment (50% PoC, 50% production).
- Implement LLM-based features such as summarization, classification, RAG (retrieval-augmented generation), conversational workflows, and enterprise automation using Python and backend frameworks.
- Build and evaluate traditional ML models (forecasting, anomaly detection, classification, clustering) to support HR, Finance, IT, and other enterprise functions.
- Translate business requirements into technical solutions, working closely with Product Owners and Solution Architects to ensure feasible designs aligned with project goals.
- Integrate AI models into existing systems, develop backend services, APIs, and data pipelines connecting internal platforms and enterprise applications.
- Perform data exploration, feature engineering, and model evaluation, ensuring the accuracy and usefulness of datasets used in AI initiatives.
- Support AI model deployment and operations by preparing data, coordinating with technical teams, and ensuring models meet reliability and performance standards.
- Conduct model testing and benchmarking, comparing approaches (LLM, ML, heuristic) and providing evidence-based recommendations.
- Monitor emerging AI capabilities and propose practical application opportunities that deliver measurable value without over-investing in complexity or unnecessary R&D.
Requirement
- Bachelor's degree in Computer Science, Data Science, Engineering, or a related technical field.
- 35 years of experience in applied machine learning, AI engineering, or backend engineering roles.
- Strong programming skills in Python and at least one backend language (NodeJS, Java, Go, or equivalent).
- Hands-on experience building applications using LLMs with OpenAI, Azure OpenAI, or AWS Bedrock APIs, along with solid knowledge of prompt design and LLM integration patterns.
- Ability to develop traditional ML models (classification, regression, anomaly detection, forecasting), including feature engineering, model evaluation, and experimentation.
- Proficient in SQL and a strong understanding of data structures to support AI and ML workflows.
- Comfortable working in a fast-paced environment, balancing experimentation with real-world deployment.
