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Visionet Systems

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3.3

based on 826 Reviews

Video summary

58 Visionet Systems Jobs

Lead - Gen AI Engineer

Visionet Systems Private Limited.

3.3

based on 826 Reviews

5-9 years

Bangalore / Bengaluru

Lead - Gen AI Engineer

Visionet Systems

posted 4d ago

Job Role Insights

Flexible timing

Key skills for the job

Data Entry Civil Engineering Data Science Python Artificial Intelligence Machine Learning

+ 13 more

Job Description

· B.E/BTech/M.C.A with 3-8 years of IT experience as solution architect / data scientist / ML Architect for large corporate/organizations
· Strong foundation in machine learning, deep learning, and neural networks.
· Solid understanding of data preprocessing, feature extraction, and model evaluation.
· Experience in training and optimizing neural networks on large datasets.
· Experience with cloud computing platforms like Azure for building and deploying Generative AI solutions
· Hands-on experience in building and deploying Machine Learning solutions using various supervised/unsupervised ML algorithms such as Linear/Logistic Regression, Support Vector Machines, (Deep) Neural Networks, Random Forest, etc
· Hands-on experience in Python programming and statistical packages, and ML libraries such as scikit-learn, Keras, TensorFlow etc.
· Hands-on experiences on Vector Databases (e.g. Azure AI Search Milvus , Weaviate, ChromaDB, Pinecone, or other vector stores)
· Strong problem-solving skills and a creative mindset.
· Excellent communication, presentation skills and teamwork abilities.

Roles and Responsibilities

As a Generative AI Engineer / Lead , you will play a crucial role in designing, developing, and implementing cutting-edge generative artificial intelligence models and systems
· Design, develop, and implement cutting-edge Generative AI solutions, including Multi-Modal Retrieval-Augmented Generation (RAG) systems using Azure native technologies
· Build and deploy Generative AI solutions in production environments, ensuring scalability, reliability and efficiency of the deployed models
· Prepare and pre-process datasets to be used for training generative models. Apply techniques such as data augmentation, normalization, and transformation to ensure optimal model performance.
· Train generative models on large-scale datasets using appropriate frameworks and libraries. Optimize models for efficiency, speed, and quality of generated output.
· Fine-tune model hyper parameters to achieve optimal performance, balancing factors such as convergence speed, stability, and output diversity.
· Develop and implement metrics to evaluate the quality, diversity, and novelty of generated content. Continuously iterate on models to improve these metrics.
· Good understanding of Multi-Agent System design, including Multi agent Design patterns communication protocols, task orchestration strategies , Memory management
· Expertise in Python and Generative AI frameworks (e.g., PyTorch, TensorFlow, LangChain, LangGraph , Vector Database – Azure AI search , Milvus , Weaviate . LLM- GPT, Llama , Anthropic ).
· Knowledge of internal working of Large Language Models , Transformer Based Models
· Stay up-to-date with the latest advancements in generative AI, machine learning, and deep learning techniques and identify opportunities to integrate them into our products and services.