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Role at a glance
You will
The ideal candidate should have hands-on experience in designing AI agents, optimizing prompts, integrating LLM APIs, and developing…
The role involves collaborating with product, engineering, and business teams to create impactful AI-driven applications and workflows.
Design, develop, and deploy Generative AI solutions using Large Language Models (LLMs).
Experience
3–6 years
Education
Bachelor's degree
Must-have skills
AWSCI/CDDatabricksDockerGitJSON
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Job description
Description
Required Technical Skills
Generative AI & LLMs
Strong understanding of:
Large Language Models (LLMs)
Prompt Engineering techniques
Retrieval-Augmented Generation (RAG)
AI Agents / Agentic AI workflows
Fine-tuning concepts and model evaluation
Programming Skills
Hands-on experience with:
Python
REST APIs
JSON handling and API integrations
AI Frameworks & Tools
Experience with:
LangChain / LangGraph/LangSmith
LlamaIndex
Semantic Kernel
Knowledge of Low Code Solution
Responsibilities
Role Overview
We are seeking a highly skilled and innovative GenAI Engineer / Prompt Engineer with 3–6 years of experience in building AI-powered applications leveraging Large Language Models (LLMs), Generative AI frameworks, and prompt engineering techniques.
The ideal candidate should have hands-on experience in designing AI agents, optimizing prompts, integrating LLM APIs, and developing scalable AI solutions for enterprise use cases. The role involves collaborating with product, engineering, and business teams to create impactful AI-driven applications and workflows.
Key Responsibilities
Design, develop, and deploy Generative AI solutions using Large Language Models (LLMs).
Create, test, and optimize prompts for accuracy, consistency, and performance.
Build AI agents and multi-agent workflows for enterprise applications.
Integrate LLM APIs such as OpenAI, Anthropic Claude, Gemini, or open-source models.
Develop Retrieval-Augmented Generation (RAG) and Agentic AI solutions.
Work on vector databases, embeddings, semantic search, and knowledge retrieval systems.
Collaborate with data engineers and application teams for end-to-end AI solution integration.
Evaluate model performance and improve outputs through prompt tuning and experimentation.
Implement guardrails, security, and responsible AI practices.
Build reusable AI workflows, templates, and accelerators.
Stay updated with emerging trends in GenAI, LLMs, and AI frameworks.
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Questions you may be asked
What is the difference between an AWS Region and an Availability Zone?
Explain the AWS shared responsibility model.
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