Senior Gen Ai

Job Description

Our client is a leading global technology and consulting company is seeking a highly skilled GenAI Specialist to join its growing AI & Data team. The organization delivers advanced data, cloud, analytics, and AI solutions across multiple industries including financial services, manufacturing, consumer products, and life sciences.

Key Responsibilities

1. Design, build, and optimize GenAI and agentic AI solutions for enterprise use cases.
2. Develop and maintain scalable microservices and AI pipelines for production environments.
3. Build and deploy machine learning models for fraud detection, compliance monitoring, and financial crime prevention.
4. Implement Generative AI solutions, including synthetic data generation and advanced NLP applications.
5. Deploy scalable ML systems capable of processing large-scale transactional data with improved operational efficiency.
6. Optimize model performance through hyperparameter tuning and advanced evaluation techniques.
7. Conduct A/B testing and performance analysis for ML-driven solutions.
8. Develop production-grade, PEP8-compliant Python code focused on scalability, readability, and maintainability.
9. Collaborate with cross-functional teams and communicate technical concepts effectively to non-technical stakeholders.

Required Skills & Experience

1. Experience with advanced LLM techniques such as RAG, Graph RAG, AI Agents, and fine-tuning approaches.
2. Strong hands-on experience with GenAI technologies and LLM ecosystems.
3. Familiarity with both open-source and commercial LLMs, including models such as Llama, Mistral, Gemma, GPT, Claude, and Gemini.
4. Expertise in Prompt Engineering, RAG pipelines, RAFT, and PEFT methods (LoRA, QLoRA, etc.).
5. Strong proficiency in Python and GenAI/NLP frameworks.
6. Experience with Generative Models including GANs, VAEs, and Transformers.
7. Solid understanding of NLP tasks including:
   Intent Recognition
   Entity Extraction
   Language Modeling
   Text Classification
   Question Answering
   Summarization
   Topic Modeling
8. Exposure to cloud technologies, APIs, Docker, and vector databases such as Qdrant and PostgreSQL is a plus.