AI Engineer

Job Description –

AI Engineer
We are seeking an experienced AI Engineer with expertise in Python and prompt
engineering. The ideal candidate will have a minimum of 3+ years of relevant experience, a
good understanding of LLMs, LangGraph, LangChain, and AutoGen is a professional who
designs, develops, and deploys intelligent systems utilizing large language models (LLMs)
and advanced AI frameworks.
 • 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.