Data Scientist (Agentic AI)

We’re looking for an Data Scientist (Agentic AI) to design, build, and deploy next‑generation AI agents powered by LLMs and Generative AI. In this role, you will work on real business challenges, create intelligent agent workflows, and help scale AI capabilities across multiple countries and domains.

What You’ll Do

• Build and deploy LLM‑powered agents that interact with data, tools, and business applications.

• Design agentic solutions that support complex workflows and decision‑making.

• Work closely with engineers to productionize your AI agents using modern development practices.

• Translate business problems into data‑driven solutions and actionable insights.

• Support teams in understanding AI agent behavior and recommending improvements.

• Strengthen the team’s overall AI craftsmanship through knowledge sharing and collaboration.

What You Bring:

• Degree in Computer Science or similar field (NLP/ML background is a plus).

• Experience building LLM, Generative AI, or NLP solutions.

• Strong Python skills and experience delivering production‑ready code.

• Understanding of LLM fundamentals and modern AI development patterns.

• Ability to build end‑to‑end AI applications and deliver business impact.

• Fluent in English; comfortable working in Agile teams.

Nice to have (but Not Required)

• Experience with agentic AI frameworks such as LangGraph, LangChain, Semantic Kernel, Autogen, CrewAI, or similar systems used to build tool‑using or multi‑step agents.

• Familiarity with cloud AI platforms like Azure AI Studio, OpenAI APIs, Google Vertex AI Agent Builder, or Google AI SDK for building and deploying agent workflows.

• Exposure to retrieval and memory systems (vector databases, embedding-based search, knowledge stores) to support grounded and context-aware agents.

• Comfort with modern workflow orchestration tools (DAGs/graphs, workflow engines, event-driven patterns) for building reliable multi-step AI flows.

• Understanding of LLM evaluation and safety practices, including structured outputs, scenario testing, and basic guardrail implementation.

• Experience integrating AI systems with real APIs, tools, databases, or business processes, especially in settings where agents take actions rather than only generate text.