About the role
ABOUT THE ROLEWe are seeking a seasoned AI Engineer to build, fine-tune, and deploy intelligent AI systems at scale. You willwork at the intersection of LLMs, machine learning, and software engineering — developing production-ready AIfeatures 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, HuggingFaceTransformers) • 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