Design, build, deploy, and maintain Artificial Intelligence solutions applied to real business problems, including predictive models, recommendation systems, intelligent assistants, and generative and agentic AI architectures. Ensure scalability, traceability, and measurable business value across enterprise environments.

• Design AI solution architectures aligned with business objectives.
• Develop, train, and optimize machine learning and deep learning models.
• Implement generative AI systems, including LLMs, Retrieval-Augmented Generation (RAG), and intelligent agents.
• Integrate AI models into web applications, APIs, and business workflows.
• Define and execute model evaluation processes, including metrics, evaluations, and testing.
• Collaborate with Data Science, Data Engineering, and Infrastructure teams to deploy AI solutions into production.
• Document models, architectural decisions, and associated technical and operational risks (AI governance).
• Continuously improve model performance, reliability, and operational efficiency.

Technical Requirements

• Strong understanding of supervised and unsupervised machine learning.
• Experience with deep learning architectures, including neural networks and transformers.
• Hands-on experience with generative AI concepts such as LLMs, embeddings, RAG, and agentic AI.
• Proficiency in Python for AI development (NumPy, Pandas, PyTorch or TensorFlow).
• Experience working with model APIs and platforms (OpenAI, Azure OpenAI, Hugging Face, or similar).
• Knowledge of MLOps / LLMOps principles, including versioning, monitoring, and retraining workflows.
• Experience designing and deploying AI solutions on cloud platforms (AWS, Azure, or GCP).

Experience

• 2+ years developing AI solutions in production environments.
• Experience integrating AI models into enterprise-grade applications.
• Experience working with real-world, incomplete, or noisy datasets.

Core Competencies

• Strong analytical and systems-thinking mindset.
• Ability to translate business problems into effective technical solutions.
• Results-oriented approach with a focus on continuous improvement.
• Clear technical communication with non-technical stakeholders.