Gen AI Developer

VDart

Gen AI Developer

Vdart Software Services Pvt Ltd

Company

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Gen AI Developer

at VDart

Agency job

via Vdart Software Services Pvt Ltd

5 - 7 yrs

₹10L - ₹20L / yr

Remote only

Skills

Generative AI

Artificial Intelligence (AI)

Machine Learning (ML)

Python

Natural Language Processing (NLP)

Large Language Models (LLM) tuning

TensorFlow

PyTorch

Vector database

Amazon Web Services (AWS)

Microsoft Windows Azure

Job Descriptions:

1. Job Requirement

We are seeking a highly skilled Senior Generative AI Developer to join our AI/ML team. This role focuses on leveraging cutting-edge Generative AI (GenAI) technologies to drive innovation and efficiency in software development and business processes. The ideal candidate will work on advanced techniques such as Retrieval-Augmented Generation (RAG), document chunking, and data embedding across multiple vector databases. They will collaborate closely with cross-functional teams to design, implement, and optimize AI-driven solutions, with exposure to cloud-native AI platforms like Amazon Bedrock and Microsoft Azure OpenAI considered a plus.

Responsibilities:

Develop and deploy GenAI-based applications to solve complex business problems.
Implement RAG frameworks to enhance information retrieval and response accuracy.
Design and optimize document chunking strategies tailored to specific data types and use cases.
Build and manage data embeddings using various vector databases for high-performance similarity searches.
Collaborate with data engineers and scientists to integrate AI solutions seamlessly into existing pipelines.
Explore and implement best practices for leveraging Amazon Bedrock and Azure OpenAI services.
Stay updated on emerging trends and technologies in the GenAI and AI/ML landscape.

2. Required Technical Skillsets

Mandatory Skills:

Proficiency in Generative AI frameworks and technologies like
Prompt Engineering – creating specific prompts to guide LLM outputs
Fine Tuning and Few-Shot learning – Adapting LLMs for domain-specific tasks.
Retrieval-Augmented Generation (RAG) – Combining LLMs with external data retrieval systems
Text-to-Text Transfer Transformation (T5) – Framework for translating tasks into text forms.
Expertise in designing and implementing chunking strategies for diverse datasets.
Strong knowledge of data embedding techniques and working experience with vector databases like Pinecone, Weaviate, or Milvus.
Solid programming skills in Python, with experience in AI/ML libraries such as TensorFlow, PyTorch, or Hugging Face Transformers.
Familiarity with cloud platforms and services for AI/ML workloads (AWS or Azure).
Experience with API integration for AI services and building scalable applications.

Desirable Skills:

Hands-on experience with Amazon Bedrock and/or Microsoft Azure OpenAI services.
Knowledge of natural language processing (NLP) techniques and large language model (LLM) fine-tuning.
Exposure to different chunking strategies, including overlap and semantic segmentation.
Understanding of DevOps practices for deploying and managing AI models in production.
Strong problem-solving and analytical skills with a focus on delivering value-driven solutions.

Job Descriptions:

1. Job Requirement

Responsibilities:

Develop and deploy GenAI-based applications to solve complex business problems.
Implement RAG frameworks to enhance information retrieval and response accuracy.
Design and optimize document chunking strategies tailored to specific data types and use cases.
Build and manage data embeddings using various vector databases for high-performance similarity searches.
Collaborate with data engineers and scientists to integrate AI solutions seamlessly into existing pipelines.
Explore and implement best practices for leveraging Amazon Bedrock and Azure OpenAI services.
Stay updated on emerging trends and technologies in the GenAI and AI/ML landscape.

2. Required Technical Skillsets

Mandatory Skills:

Proficiency in Generative AI frameworks and technologies like
Prompt Engineering – creating specific prompts to guide LLM outputs
Fine Tuning and Few-Shot learning – Adapting LLMs for domain-specific tasks.
Retrieval-Augmented Generation (RAG) – Combining LLMs with external data retrieval systems
Text-to-Text Transfer Transformation (T5) – Framework for translating tasks into text forms.
Expertise in designing and implementing chunking strategies for diverse datasets.
Strong knowledge of data embedding techniques and working experience with vector databases like Pinecone, Weaviate, or Milvus.
Solid programming skills in Python, with experience in AI/ML libraries such as TensorFlow, PyTorch, or Hugging Face Transformers.
Familiarity with cloud platforms and services for AI/ML workloads (AWS or Azure).
Experience with API integration for AI services and building scalable applications.

Desirable Skills:

Hands-on experience with Amazon Bedrock and/or Microsoft Azure OpenAI services.
Knowledge of natural language processing (NLP) techniques and large language model (LLM) fine-tuning.
Exposure to different chunking strategies, including overlap and semantic segmentation.
Understanding of DevOps practices for deploying and managing AI models in production.
Strong problem-solving and analytical skills with a focus on delivering value-driven solutions.

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