Artificial Intelligence Engineer

AI Engineer

• Python Proficiency:

Strong programming skills in Python are fundamental for developing and implementing AI solutions.

• Prompt Engineering:

Expertise in crafting effective prompts to guide LLMs towards generating desired and accurate responses, often involving techniques like prompt chaining and optimization.

• LLM Application Development:

Hands-on experience in building applications powered by various LLMs (e.g., GPT, LLaMA, Mistral). This includes understanding LLM architecture, memory management, and function/tool calling.

• Agentic AI Frameworks:

Proficiency with frameworks designed for building AI agents and multi-agent systems, such as:

• LangChain:

  A framework for developing applications powered by language models, enabling chaining of components and integration with various tools and data sources.

• LangGraph:

  An extension of LangChain specifically designed for building stateful, multi-actor applications using LLMs, often visualized as a graph of interconnected nodes representing agents or logical steps.

• AutoGen:

  A Microsoft framework that facilitates multi-agent collaboration, allowing specialized agents to work together to solve complex problems through task decomposition and recursive feedback loops.

• Retrieval-Augmented Generation (RAG):

Experience in implementing and optimizing RAG pipelines, which combine LLMs with external knowledge bases (e.g., vector databases) to enhance generation with retrieved information.

• Deployment and MLOps:

Practical knowledge of deploying AI models and agents into production environments, including containerization (Docker), orchestration (Kubernetes), cloud platforms (AWS, Azure, GCP), and CI/CD pipelines.