Generative AI Engineer
Tekskills Inc.
4 - 6 years
Pune
Posted: 20/03/2026
Job Description
We are seeking an AI Engineer to join our AI Metadata Augmentation team.
Exp 4-6 Years
Location: Pune
In this role, you will leverage Large Language Models (LLMs) and AI Agents on AWS to automate the enhancement of metadata for various dataset types.
The ideal candidate has a background in Data Science, Data Engineering, or Software Engineering and is eager to build scalable AI solutions in the cloud.
Key Responsibilities
* AI Agent Development: Design and deploy AI Agents and LLM-based workflows on AWS to automatically generate, correct, and augment metadata.
* Solution Testing & Verification: Implement testing frameworks to validate AI performance. You will actively audit and verify the AI agent's output to ensure data accuracy and reliability
* Data Handling: Write efficient Python scripts to parse, manipulate, and restructure complex data (specifically JSON) for model consumption and storage.
* Cloud Engineering: Utilise AWS services to build secure environments; manage permissions (IAM) and configure necessary cloud resources.
* Infrastructure as Code: Provision and manage infrastructure using IaC to ensure reproducible and scalable deployments.
Qualifications & Skills
Background & Experience
* Previous experience as a Data Scientist, Data Engineer, or Software Engineer.
* Good understanding of AI and ML concepts.
* AWS AI Expertise: Proven professional experience or hands-on training with Generative AI on AWS (specifically LLMs and AI Agents).
Technical Requirements
* Python: Intermediate to Advanced proficiency. You should be comfortable writing clean, production-ready code.
* AWS Fundamentals: Solid understanding of the AWS ecosystem.
* Infrastructure as Code (IaC): Basic ability to provision infrastructure via code (using tools like CloudFormation, CDK, or Terraform).
* Data Formats: Strong capability in working with and manipulating JSON data structures.
Nice to Have
* Experience with AWS Bedrock or SageMaker.
* Knowledge of vector databases and RAG (Retrieval-Augmented Generation) architectures
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