Analytics Lead at Create building and scaling the analytics foundation. Partnering with stakeholders to deliver insights and self - service analytics tools.
About the role
- AI Engineer designing and operationalizing AI-driven solutions for analytics. Collaborating with data scientists and MLOps engineers in innovative projects.
Responsibilities
- Develop AI Agents: Design, code, and implement AI agents and copilots using Google's Gemini API and Microsoft Copilot Studio.
- Integrate Systems: Write robust Python code to connect AI agents with enterprise data sources, internal tools, and third-party services via REST APIs.
- Implement RAG Patterns: Build and refine Retrieval-Augmented Generation (RAG) pipelines to ensure agents provide accurate, context-aware responses grounded in proprietary data.
- Prompt Engineering: Craft, test, and iterate on effective prompts to guide agent behavior, ensuring reliability, safety, and desired outcomes.
- Full Lifecycle Development: Participate in the entire development lifecycle, from initial concept and design through to testing, deployment, and maintenance.
- Collaboration: Work closely with senior engineers to overcome technical challenges and with product managers to translate business requirements into functional agent capabilities.
- Troubleshooting: Debug and resolve issues in agent performance, whether they stem from the underlying LLM, the data pipeline, or the integration code.
- Work with analytics, product, and engineering teams to define and deliver AI solutions.
- Participate in architecture reviews and iterative development cycles.
- Support knowledge sharing and internal GenAI capability building.
Requirements
- Bachelor's degree in Computer Science, Software Engineering, or a related field.
- 2-4 years of professional software development experience.
- Strong programming proficiency in Python and a solid understanding of object-oriented principles.
- At least 1 year of hands-on experience building applications with Large Language Models (LLMs) through professional work, significant personal projects, or open-source contributions.
- Solid understanding of core LLM concepts, including prompt engineering, embeddings, and function calling/tool use.
- Experience consuming and interacting with REST APIs.
- A proactive, problem-solving mindset and a strong desire to learn and adapt in a fast-evolving field.
- Direct experience making API calls to Google's Gemini, OpenAI models, or using Microsoft Copilot Studio/Azure OpenAI Service.
- Familiarity with agentic frameworks like LangChain, LlamaIndex, or Microsoft's Semantic Kernel.
- Experience with cloud services on GCP (like Vertex AI) or Azure.
- Knowledge of vector databases (e.g., Pinecone, Chroma, Weaviate) and how they fit into RAG architectures.
- Basic understanding of CI/CD pipelines and containerization (Docker).
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