Experience - 7+ Years Looking for Immediate Joiners.

Onsite role, 5 Days working. :

We are seeking a seasoned AI Engineer to build, fine-tune, and deploy intelligent AI systems at scale. You will work at the intersection of LLMs, machine learning, and software engineering — developing production-ready AI features and pipelines that power our core product. Key Responsibilities:

- Design, develop, and deploy AI/ML models and pipelines in production environments
- Implement Retrieval-Augmented Generation (RAG) architectures and agentic AI workflows
- Fine-tune and optimize LLMs for domain-specific use cases using RLHF, LoRA, QLoRA
- Build robust prompt engineering frameworks and evaluation pipelines
- Integrate LLM APIs (OpenAI, Claude, Gemini) and open-source models into product features
- Develop and maintain vector search infrastructure and embedding pipelines
- Collaborate with architects, backend engineers, and product teams on AI feature delivery
- Monitor model performance, conduct A/B testing, and iterate based on metrics
- Implement guardrails, safety layers, and hallucination-mitigation strategies
- Contribute to MLOps practices: model versioning, deployment pipelines, monitoring KEY SKILLS & REQUIREMENTS:
- Strong expertise in Python,



with deep knowledge of AI/ML libraries (PyTorch, TensorFlow,

HuggingFace Transformers)

- Hands-on experience with LLM APIs and prompt engineering techniques (CoT, few-shot, ReAct)
- Experience with RAG systems, embedding models (text-embedding-3, BGE, Cohere), and vector stores
- Knowledge of agentic frameworks: LangChain, LlamaIndex, AutoGen, CrewAI, or Semantic Kernel
- Familiarity with fine-tuning techniques: LoRA, QLoRA, PEFT, instruction tuning
- Experience deploying models on cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML)
- Understanding of data preprocessing, feature engineering, and model evaluation metrics
- Proficiency with MLOps tools: MLflow, DVC, Weights & Biases, BentoML
- Experience with containerization and orchestration: Docker, Kubernetes
- Solid debugging and experimentation skills with Jupyter, FastAPI, Streamlit NICE TO HAVE:
- Experience with multi-modal models (vision-language models, Whisper, DALL-E)
- Published papers or Kaggle/competition achievements
- Exposure to speech AI, computer vision, or NLP specializations
- Knowledge of responsible AI, fairness metrics, and bias mitigation.

📌 AI Engineer (Bengaluru)
🏢 VAYUZ Technologies
📍 Bengaluru