Project Description

Our client, a global leader in the Agriculture sector, is modernizing its digital landscape and accelerating the adoption of Artificial Intelligence and innovative technologies across business processes.

We are looking for a Python / AI Engineer with strong expertise in building and integrating Large Language Models (LLMs) to join the R&D team. The successful candidate will design and implement cutting-edge AI solutions, develop Proofs of Concept (PoCs), and work closely with stakeholders to translate research into production-ready applications.

This role offers the opportunity to work on next-generation AI initiatives and influence the strategic adoption of intelligent systems in the agricultural domain.

Responsibilities

• Design, develop, and present Proofs of Concept (PoCs) for business stakeholders.
• Build, test, and deploy innovative AI-powered solutions using state-of-the-art models.
• Integrate Large Language Models (LLMs) into new and existing enterprise applications.
• Develop new functionalities and modules for internal R&D applications.
• Promote and implement best practices across AI/ML development workflows.
• Collaborate closely with business and technical teams to align AI initiatives with strategic goals.

Mandatory Skills

• 6+ years of experience in AI / Machine Learning development.
• Advanced proficiency in Python (including modern libraries and frameworks).
• Strong knowledge of AI frameworks such as TensorFlow and PyTorch.
• Expertise in LLMs (e.g., GPT, Claude, LLaMA, Gemini).
• Solid foundation in Data Science, statistical modeling, and data-driven design.
• Experience with Agentic Workflows and LangChain-based agents.
• Hands-on experience with LangGraph (e.g., hackathons, MCPs, or R&D prototypes).
• Deep understanding of Transformer architectures and fine-tuning techniques.

Nice-to-Have Skills

• Experience deploying AI models in cloud environments (AWS, Azure, or GCP).
• Familiarity with vector databases (e.g., FAISS, Pinecone, Chroma).
• Knowledge of prompt engineering and retrieval-augmented generation (RAG) pipelines.
• Experience working with cross-functional AI research teams.