Applied AI Engineer

• Apply established AI and data science techniques to design, build, test, and support AI-enabled solutions aligned to business requirements.

• Solve common and occasionally complex technical problems related to machine learning models, LLM-based solutions, agentic workflows, and data pipelines, using sound engineering judgment.

• Recommend alternative technical approaches, model configurations, or solution designs when standard methods are insufficient or suboptimal.

• Contribute across the solution lifecycle including experimentation, development, validation, documentation, and deployment support.

• Perform data analysis, feature preparation, and model evaluation to support AI-driven use cases.

• Develop clear technical documentation, dashboards, or summaries to communicate results to technical and non-technical stakeholders

• Provide technical assistance, code reviews, or day-to-day guidance to junior developers, interns, or new team members when required

Agentic & Conversational AI

• Design and develop multi-step agentic AI pipelines using orchestration frameworks such as LangChain, LangGraph, AutoGen, or Semantic Kernel.

• Build and maintain conversational AI assistants and chatbots, including retrieval-augmented generation (RAG) architectures.

• Engineer, test, and refine prompts for Claude, GPT, and Copilot; implement few-shot, chain-of-thought, and structured output strategies.

• Integrate LLMs into custom AI products via REST APIs, SDKs, and tool-use / function-calling interfaces

Data Science & Analytics

• Perform exploratory data analysis, feature engineering, and statistical modelling to derive business insights.

• Build and evaluate machine learning models (classification, regression, NLP) and document results clearly for technical and non-technical stakeholders.

• Maintain clean, versioned datasets and support data pipeline tasks in collaboration with data engineering teams

Reporting & Visualization

• Develop lightweight dashboards and reports using Power BI, Tableau, or Python-based visualization libraries

• Translate analytical findings into clear visual narratives for business audiences.

RPA & Workflow Automation

• Support the integration of robotic process automation (RPA) solutions on platforms such as UiPath and Power Automate, into end to end AI products

• Identify repetitive manual processes and propose AI-assisted automation enhancements

Experimentation & Innovation

• Continuously evaluate emerging AI tools, frameworks, and research papers; propose and drive experiments to show and validate the capabilities

  • Document learnings, support in maintaining internal knowledge repositories, and contribute to team capability building.