The Role
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.
Summary Generated by Built In
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.
Skills Required
- 3+ years of relevant AI/LLM engineering experience
- Strong proficiency in Python
- Expertise in prompt engineering (prompt chaining and optimization)
- Hands-on experience building LLM applications (GPT, LLaMA, Mistral)
- Proficiency with LangChain, LangGraph, and AutoGen
- Experience implementing Retrieval-Augmented Generation (RAG) and working with vector databases
- Deployment and MLOps experience: Docker, Kubernetes, cloud platforms (AWS, Azure, GCP), and CI/CD pipelines
- Understanding of LLM architecture, memory management, and function/tool calling
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The Company
What We Do
Technology, society, economy, policy – all moving at breakneck speed in our 21st century world. You’re feeling the pressure to quickly implement new business models, find new value, make split-second informed decisions and keep one step ahead of customers. How? The answer lies in the ability to make quick, accurate and sustainable business decisions. We believe digital offers a way of doing things better – but the journey to transformation doesn’t have to be painful. At Aligned Automation, we work hard to digitally enable your business strategy – connecting processes, technologies and people to unlock value and drive critical business outcomes.
