Design, develop, and deploy LLM-powered applications and agentic systems. Build and optimize prompts, implement RAG pipelines with vector DBs, and productionize models using containerization, orchestration, cloud platforms, and CI/CD practices.
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.
• 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.
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What you need to know about the Pune Tech Scene
Once a far-out concept, AI is now a tangible force reshaping industries and economies worldwide. While its adoption will automate some roles, AI has created more jobs than it has displaced, with an expected 97 million new roles to be created in the coming years. This is especially true in cities like Pune, which is emerging as a hub for companies eager to leverage this technology to develop solutions that simplify and improve lives in sectors such as education, healthcare, finance, e-commerce and more.
