GenAI & ML Engineer

About the Role We're looking for a well-rounded AI Engineer who brings hands-on experience across the full spectrum — from classical Machine Learning and Deep Learning to modern Generative AI.

You'll build intelligent systems that combine the best of all three worlds. Key Responsibilities Design and deploy end-to-end GenAI applications using LLMs (OpenAI, Anthropic, Gemini, open-source models) Build RAG pipelines, agentic workflows, and prompt engineering frameworks for production Train, fine-tune, and evaluate Deep Learning models (CNNs, RNNs, Transformers, diffusion models) Develop and ship classical ML models — regression, classification, clustering, ensemble methods Build and maintain data

preprocessing, feature engineering, and model training pipelines Collaborate with product and data teams to convert business problems into AI solutions Monitor model performance, run A/B experiments, and continuously improve accuracy and efficiency Apply responsible AI practices — guardrails, bias detection, hallucination mitigation

Requirements 2–5 years of experience in ML/DL/AI engineering Strong proficiency in Python Machine Learning: Scikit-learn, XGBoost, feature engineering, model evaluation Deep Learning: PyTorch or TensorFlow, CNNs, RNNs, Transformers, transfer learning GenAI: LLM APIs, LangChain / LlamaIndex, RAG, vector databases (Pinecone, Weaviate, ChromaDB) Solid understanding of statistics, linear algebra,

and model optimization Experience with cloud platforms (AWS / GCP / Azure) Familiarity with MLOps tools — MLflow, DVC, or similar Nice to Have Fine-tuning experience (LoRA, PEFT, RLHF) Multi-modal model experience (vision + language) Experience with agentic systems and multi-agent frameworks (CrewAI, AutoGen) Contributions to open-source AI/ML projects