Job Description

• Develop AI/ML PoCs and production-ready solutions across LLM and traditional machine learning domains, balancing experimentation with real-world implementation (50% PoC, 50% production).
• Implement LLM-based features such as summarization, classification, retrieval-augmented generation (RAG), conversational workflows, and enterprise automations using Python and backend frameworks.
• Build and evaluate traditional ML models (forecasting, anomaly detection, classification, clustering) to support HR, Finance, IT, and other corporate functions.
• Translate business requirements into technical solutions, working closely with Product Owners and Solution Architects to ensure design feasibility and alignment with project goals.
• Integrate AI models into existing systems, developing backend services, APIs, and data workflows that connect internal corporate platforms and in-house applications.
• Run data exploration, feature engineering, and model evaluation, ensuring the correctness and usefulness of datasets used across AI initiatives.
• Support deployment and operationalization of AI models by preparing documentation, collaborating with engineering teams, and ensuring models meet reliability and performance standards.
• Conduct model experiments and benchmarking, comparing approaches (LLM, ML, heuristics) and presenting evidence-driven recommendations.
• Maintain awareness of new AI capabilities, proposing practical opportunities that deliver measurable value without over-investing in unnecessary complexity or R&D.

Requirement

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical field.
• 3–5 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 similar).
• Hands-on experience building LLM-powered applications using OpenAI, Azure OpenAI, or AWS Bedrock APIs, with solid understanding 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.
• Proficiency in SQL and good understanding of how datasets need to be structured for AI and ML workflows.
• Comfortable working in fast-paced environments, balancing experimentation with practical delivery and alignment to business priorities.