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.
United Kingdom
India
United States