AI/ML Engineer - Data Modeling

Job Description : Job Title : AI/ML Engineer Experience : 4+ Years Location : Bangalore - Hybrid Notice Period : Immediate Joiner Job Role : We are looking for skilled AI/ML Engineers with strong hands-on experience in LLMs, embeddings, and related frameworks. The ideal candidate should demonstrate practical expertise in at least 4-5 of the listed key skills. This role requires active involvement in building, fine-tuning, and deploying language and vision models, while working in a collaborative hybrid setup. Responsibilities : - Fine-tune and train Large Language Models (LLMs), Small Language Models (SLMs), and Vision Language Models (VLMs) using frameworks like Hugging Face, PyTorch, or TensorFlow - Work on integrating LLMs with embeddings, transformers, and vector databases - Build and manage MLOps pipelines and scalable data engineering workflows - Apply techniques such as prompt tuning, prompt chaining, and agent-based approaches for solving real-world use cases - Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions - Deploy and monitor models in production environments - Perform POCs and implement the best architectural choices for model deployment Requirements : - 4+ years of hands-on experience in AI/ML engineering Proficient in at least 4-5 of the following : - Fine-tuning LLMs/SLMs/VLMs using Hugging Face, PyTorch, TensorFlow - Embeddings, Transformers - Vector databases (e.g., FAISS, Pinecone) - Prompt engineering (prompt tuning, chaining, agent-based models) - MLOps & Data Engineering workflows - Strong Python programming skills - Experience in model deployment and performance optimization - Solid understanding of NLP and/or multimodal AI systems - Location : Bangalore (Hybrid mode) Technical Skills : Fine-tuning, LLMs, SLMs, VLMs, HuggingFace, PyTorch, TensorFlow, Embeddings, Transformers, VectorDBs, FAISS, Pinecone, MLOps, CI/CD, Deployment, Monitoring, DataOps, ETL, Prompting, Prompt-Tuning, Prompt-Chaining, Agents, Evaluation, Optimization, Python, APIs Job Description : Job Title : AI/ML Engineer Experience : 4+ Years Location : Bangalore - Hybrid Notice Period : Immediate Joiner Job Role : We are looking for skilled AI/ML Engineers with strong hands-on experience in LLMs, embeddings, and related frameworks. The ideal candidate should demonstrate practical expertise in at least 4-5 of the listed key skills. This role requires active involvement in building, fine-tuning, and deploying language and vision models, while working in a collaborative hybrid setup. Responsibilities : - Fine-tune and train Large Language Models (LLMs), Small Language Models (SLMs), and Vision Language Models (VLMs) using frameworks like Hugging Face, PyTorch, or TensorFlow - Work on integrating LLMs with embeddings, transformers, and vector databases - Build and manage MLOps pipelines and scalable data engineering workflows - Apply techniques such as prompt tuning, prompt chaining, and agent-based approaches for solving real-world use cases - Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions - Deploy and monitor models in production environments - Perform POCs and implement the best architectural choices for model deployment Requirements : - 4+ years of hands-on experience in AI/ML engineering Proficient in at least 4-5 of the following : - Fine-tuning LLMs/SLMs/VLMs using Hugging Face, PyTorch, TensorFlow - Embeddings, Transformers - Vector databases (e.g., FAISS, Pinecone) - Prompt engineering (prompt tuning, chaining, agent-based models) - MLOps & Data Engineering workflows - Strong Python programming skills - Experience in model deployment and performance optimization - Solid understanding of NLP and/or multimodal AI systems - Location : Bangalore (Hybrid mode) Technical Skills : Fine-tuning, LLMs, SLMs, VLMs, HuggingFace, PyTorch, TensorFlow, Embeddings, Transformers, VectorDBs, FAISS, Pinecone, MLOps, CI/CD, Deployment, Monitoring, DataOps, ETL, Prompting, Prompt-Tuning, Prompt-Chaining, Agents, Evaluation, Optimization, Python, APIs