Generative AI Engineer, Data & Analytics

Key Responsibilities

Functional Responsibilities

• Scale analytics capabilities across business functions and shape the organization's data strategy.
• Organize, process, and analyze large datasets across various platforms.
• Extract and communicate key insights to influence product and business strategies.
• Collaborate with external vendors/partners on technical scope, approach, and deliverables.
• Develop proof-of-concept models to validate innovative solutions.
• Translate business requirements into analytical approaches and reporting solutions.
• Design and implement advanced statistical tests and models for actionable problem-solving.
• Present insights and recommendations to senior leadership in a clear and impactful manner.

AI/ML & LLM Responsibilities

• Build and optimize data pipelines for ML and LLM use cases (dataset creation, cleaning, augmentation, labeling workflows).
• Apply advanced machine learning algorithms and statistical techniques (e.g., XGBoost, SVM, regression, segmentation, forecasting, A/B testing).
• Work hands-on with modern LLMs (OpenAI, LLaMA, Mistral, Anthropic) and vector databases (FAISS, Pinecone, Milvus).
• Implement fine-tuning methods like LoRA, PEFT, and adapters.
• Integrate AI/ML models into systems using APIs, batching, and streaming mechanisms.
• Ensure AI solutions are secure, safe, and compliant (e.g., hallucination reduction, PII redaction).
• Use MLOps tools for scalable deployment (Docker, Kubernetes, CI/CD pipelines).
• Work with frameworks like LangChain, Hugging Face Transformers, and Ray/Serve.