Description :

Generative AI (Gen AI) Engineer

Role Overview :

The Generative AI Engineer is a highly specialized, senior role requiring 812 years of experience with a deep focus on designing, developing, and deploying cutting-edge LLM-based solutions.

Based in Bangalore, this position demands hands-on expertise across the entire Gen AI lifecycle, from foundational model optimization and fine-tuning to building robust Retrieval-Augmented Generation (RAG) systems and production MLOps practices.

The incumbent will be critical in shaping the organization's approach to reliable, safe, and scalable AI development.

Job Summary :

We are seeking an expert Gen AI Engineer (8-12 years) with a deep technical understanding of foundational LLMs, transformer architectures, and advanced representation learning. The ideal candidate will have proven hands-on experience in building production-ready RAG systems using vector databases and complex retrieval models. Key responsibilities include implementing sophisticated fine-tuning techniques (LoRA, PEFT), designing rigorous evaluation pipelines, and deploying LLM systems using modern MLOps practices, demonstrating proficiency in Python and PyTorch/TensorFlow.

Key Responsibilities and Technical Deliverables :

- Foundational LLM Expertise : Apply a Deep understanding of foundational LLMs, including underlying architectures (e.g., Transformer), tokenization, and the nuances between pre-training vs fine-tuning. Optimize models for performance via context window optimization.

- RAG System Implementation : Demonstrate Strong experience with RAG systems, including the selection and implementation of vector DBs, strategies for hybrid retrieval, advanced embeddings, and sophisticated ranking models for information retrieval.

- Model Fine-Tuning and Adaptation : Provide Hands-on expertise in fine-tuning approaches such as instruction tuning, LoRA (Low-Rank Adaptation), PEFT (Parameter-Efficient Fine-Tuning), model merging, distillation, and prompt-tuning to adapt models for specific tasks.

- Evaluation Pipeline Design : Possess the Proven ability to design and implement evaluation pipelines using both quantitative and qualitative metrics to assess model performance across dimensions like truthfulness, reasoning quality, safety, and alignment.

- Core Machine Learning Foundations : Leverage a Strong background in representation learning, transformers, IR/retrieval models, and embeddings to build innovative and highly accurate solutions.

- Production MLOps for LLMs : Implement and maintain robust MLOps practices specific to LLM systems, including model serving, monitoring, and observability for performance, drift, and safety in production.

- Development Stack Proficiency : Maintain Proficiency in Python, core ML libraries such as PyTorch/TensorFlow, and experience integrating solutions into production ML pipelines.

Mandatory Skills & Qualifications :

- Experience : 8 to 12 years of relevant experience.

- LLM Fundamentals : Deep understanding of foundational LLMs (architectures, tokenization, pre-training vs fine-tuning).

- Retrieval Systems : Strong experience with RAG systems (vector DBs, hybrid retrieval, embeddings, ranking models).

- Fine-tuning : Hands-on expertise in fine-tuning approaches (instruction tuning, LoRA, PEFT, model merging).

- Foundational ML : Strong background in representation learning, transformers, IR/retrieval models, and embeddings.

- Development Stack : Proficiency in Python, PyTorch/TensorFlow, and production ML pipelines.

- Operations : Experience with MLOps practices : model serving, monitoring, and observability for LLM systems.

Preferred Skills :

- Experience with cloud platforms (AWS, Azure, or GCP) for deploying large-scale ML services.

- Knowledge of prompt engineering techniques and best practices.

- Experience with specialized libraries for LLM development (e.g., Hugging Face ecosystem, LangChain, LlamaIndex).

- Experience implementing safety and guardrail mechanisms (e.g., content filtering, adversarial testing).