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