As a Generative AI Engineer at EXL, you will spearhead the design, development, and deployment of cutting-edge LLM-based solutions that solve complex business challenges for our global clientele. You will operate at the forefront of innovation, bridging the gap between experimental AI research and production-grade enterprise applications. Working closely with cross-functional teams of data scientists, domain experts, and business stakeholders, you will translate ambiguous requirements into robust, scalable AI architectures that significantly enhance operational efficiency and customer experience.

Key Responsibilities :

- Architect and implement end-to-end Generative AI pipelines, including RAG systems and fine-tuning frameworks, to deliver high-impact business solutions for enterprise clients.

- Evaluate and integrate state-of-the-art Large Language Models and open-source frameworks to ensure optimal performance, cost-efficiency, and scalability for production environments.

- Collaborate with product managers and business stakeholders to identify high-value use cases, ensuring that AI-driven outputs align with specific industry requirements and regulatory standards.

- Establish rigorous evaluation frameworks and monitoring protocols to maintain model accuracy, mitigate hallucinations, and ensure the ethical deployment of AI systems.

- Mentor junior engineers and foster a culture of technical excellence by conducting code reviews, sharing best practices, and staying abreast of the rapidly evolving Generative AI landscape.

Required Skillset :

- Demonstrated expertise in building and deploying LLM-based applications using frameworks such as LangChain, LlamaIndex, or Haystack, with a strong grasp of vector databases like Pinecone, Milvus, or Weaviate.

- Advanced proficiency in Python and deep learning libraries including PyTorch or TensorFlow, combined with a solid understanding of transformer architectures and attention mechanisms.

- Proven ability to translate complex technical concepts into actionable business insights, facilitating effective communication with non-technical stakeholders and senior leadership.

- Strong analytical mindset with an advanced degree in Computer Science, Data Science, or a related quantitative field, reflecting a deep foundation in machine learning theory.

- Adaptability to work in a hybrid environment in Bangalore, demonstrating the ability to collaborate effectively across distributed teams while maintaining high standards of delivery and innovation.