Ai Ml Engineer_Level 3

Job description

**Job Requirements\\*** 1. Programming: Python (strong), SQL 2. ML Frameworks: Scikitlearn, TensorFlow / PyTorch 3. GenAI / LLMs: OpenAI / Azure OpenAI, LangChain, vector databases 4. Data Engineering: ETL pipelines, data modeling, data validation 5. Cloud: Azure / AWS / GCP (at least one) **Key Responsibilities\\*** 1. Design, develop, and deploy machine learning models for prediction, classification, and optimization use cases 2. Apply supervised, unsupervised, and timeseries techniques 3. Evaluate models using appropriate metrics (accuracy, precision/recall, RMSE, Sharpe ratio, etc.) 4. Build and optimize LLM-based applications (chatbots, assistants, summarization, Q&A) 5. Implement Retrieval Augmented Generation (RAG) using vector databases 6. Work with prompt engineering, model evaluation, and LLM guardrails 7. Integrate LLMs with tools, APIs, and enterprise systems 8. Perform feature engineering and model tuning 9. Participate in code reviews and contribute to architecture decisions. 10. Experience with financial domain data (risk, portfolio, pricing, forecasting) 11. Knowledge of model evaluation frameworks and LLMasjudge concepts 12. Exposure to Hugging Face, finetuning, or opensource LLMs 13. Experience with Spark / Databricks **Section IV - Job Qualifications & Skills** **Section** **Details / Example Content** **Domain** Highly skilled **ML / AI Engineer** with strong experience in **Machine Learning, Generative AI, and ETL/Data Engineering**. The candidate will design, build, and deploy scalable AI/ML solutions, work with structured and unstructured data pipelines, and contribute to GenAI use cases such as LLM-based applications, RAG systems, and intelligent automation.