Agentic AI Engineer

Role Overview

The Agentic AI Engineer is tasked with the design, development, and deployment of advanced AI solutions utilizing large language models (LLMs), diffusion models, and other generative AI technologies. This position requires close collaboration with teams across data science, software engineering, and business domains to develop intelligent, context-sensitive applications that drive automation, foster creativity, and improve decision-making at scale.

Key Responsibilities

• Design, fine-tune, and implement LLMs and generative models tailored to specific organizational needs.
• Craft and optimize prompt engineering techniques and model interaction methods to enhance result accuracy and relevance.
• Develop APIs, data pipelines, and integration frameworks to embed generative AI features into products and workflows.
• Coordinate with data engineers to secure and curate high-quality, domain-specific datasets suitable for training and evaluating models.
• Adhere to responsible AI standards addressing bias mitigation, model explainability, and robust data governance.
• Continuously monitor and iteratively refine model performance through feedback mechanisms and retraining.
• Keep current with advancements and emerging tools in AI research such as LangChain, Hugging Face, and OpenAI APIs.
• Support organizational knowledge sharing and develop best practices to enable scalable AI solution deployment.

Skill and Expertise Requirements

• Advanced proficiency in Python and deep learning frameworks like PyTorch or TensorFlow.
• Knowledge of SQL, machine learning concepts, big data technologies, and various databases.
• Hands-on experience with large language model APIs including OpenAI, Anthropic, Gemini, and vector databases such as Pinecone, FAISS, or Weaviate.
• Experience with prompt engineering, fine-tuning of models, and reinforcement learning guided by human feedback (RLHF).
• Strong grounding in natural language processing (NLP), deep learning architectures, and the design of data pipelines.
• Exposure to cloud computing platforms (AWS, Azure, GCP) and MLOps tools for deploying AI models at scale.
• Excellent analytical skills and effective problem-solving capabilities.

Eligibility Criteria

Applicants should hold at least a graduate degree in any discipline.