Gen AI/ML EngineerSN Required Information Details
1 Role Gen AI / ML Engineer Level 3
2 Required Technical Skill Set 1. Programming: Python (strong), SQL 2. ML Frameworks: Scikit-learn, 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)
3 No. of Requirements 2
4 Desired Experience Range 5 to 7 years of experience
5 Location of Requirement India Bengaluru
Desired Competencies (Technical/Behavioral Competency)
Must-Have 1. Programming: Python (strong), SQL 2. ML Frameworks: Scikit-learn, 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) 6. Design and maintain ETL / ELT pipelines for large-scale data ingestion 7. Work with structured and semi-structured data (CSV, JSON, Parquet) 8. Ensure data quality, lineage, and reliability 9. Optimize pipelines for performance and scalability 10. Deploy ML/AI models using APIs or batch pipelines 11. Implement CI/CD for ML workflows 12. Monitor model performance, drift, and data issues
Role
descriptions /
Expectations
from the Role 1. Design, develop, and deploy machine learning models for prediction, classification, and optimization use cases 2. Apply supervised, unsupervised, and time-series 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 LLM-as-judge concepts 12. Exposure to Hugging Face, fine-tuning, or open-source LLMs 13. Experience with Spark / Databricks

Keyskills: Pytorch LangChain Aiml Python Generative Ai vector databases azure open ai