AI Engineer - Machine Learning Models

- 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