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 (Py. Torch, Tensor. Flow, Hugging. Face Transformers)
• Hands-on experience with LLM APIs and prompt engineering techniques (CoT, few-shot, Re. Act)
• Experience with RAG systems, embedding models (text-embedding-3, BGE, Cohere), and vector stores
• Knowledge of agentic frameworks: Lang. Chain, Llama. Index, Auto. Gen, CrewAI, or Semantic Kernel
• Familiarity with fine-tuning techniques: LoRA, QLoRA, PEFT, instruction tuning
• Experience deploying models on cloud platforms (AWS Sage. Maker, 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