AI Engineer

Location Bangalore Experience 7–14 Years Employment Type Full-Time ABOUT THE ROLE 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 • Strong 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