Summary of the role:
At EXL, we are developing solutions for some of the most exciting and challenging AI and NLP business problems. If you want to push the existing AI and Gen-AI knowledge boundaries, then we have an excellent opportunity for you. We are looking for AI/Gen-AI/Agentic-AI Architect, you will be responsible for designing, developing, and deploying generative AI solutions. This includes working with stakeholders to understand business requirements, designing and deploying generative AI models to solve complex business problems, mentoring and leading a team of data scientists to deliver generative AI projects. The ideal candidate will have a strong understanding of generative AI techniques, as well as experience in software engineering and machine learning. The ideal candidate will possess a strong background in natural language processing, large language models, deep learning, excellent problem-solving skills, and a passion for pushing the boundaries of AI research.
As part of your duties, you will be responsible for:
- Develop, fine-tune, context tune and implement state-of-the-art NLP models including Large Language Models like GPT, Llama, Claude, BLOOM, Flan-T5, Falcon etc.
- Hands on experience with complementary technologies around LLMs like embedders, vector databases(chroma, weaviate etc.), orchestration tools like Langchain, LangGraph, LlamaIndex etc.
- Hands on experience with building AI agents using frameworks like Google ADK, Azure AutoGen, Crew.AI, in combination of LangGraph and LangChain, etc. Have interacted with MCP servers for artifacts/data processing, tools calling, and AI observability.
- Experience of architecting AI systems to solve complex business problems.
- Conduct research and experimentation to improve existing models and propose novel approaches.
- Collaborate with cross-functional teams to integrate generative AI solutions into real-world applications.
- Stay up-to-date with the latest advancements in deep learning and generative models and apply them to enhance our AI capabilities.
- Provide deep leadership and coaching in the project delivery lifecycle. Focus on shared learning, continuous improvement, and drive adoption of best practices.
Experience we consider to be essential for the role:
- Hands-on experience with current deep learning frameworks (e.g., PyTorch, TensorFlow) as evidenced by released code (e.g., GitHub repositories – version control awareness).
- Solid understanding of optimization techniques for training deep neural networks, regularization methods, and hyperparameter/fine tuning.
- Experience in Generative AI Models and LLMs, finetuning LLMs, prompt engineering and experience with LLM orchestration frameworks like Langchain, LlamaIndex etc.
- Experience in using LLMs using API and used LLMs in building AI solutions from OpenAI, Google, etc.
- Strong software engineering skills for rapid and accurate development of AI models and systems.
- Provide business-oriented solution with ability to communicate effectively, both verbally and in writing, with technical and non-technical stakeholders.
- Experience working in a collaborative environment, contributing to multidisciplinary teams and projects.
- Proven ability to solve complex problems, think creatively, and adapt to evolving research trends.
Skills and Personal attributes we would like to have:
- Master's degree in computer science, machine learning, or a related field
- 10-12 years of experience in AI, NLP including transformer architecture and LLMs, Computer Vision and related technologies
- Experience in ML Engineering and MLOps
- Strong understanding of statistical and machine learning concepts
- Experience with deep learning frameworks such as TensorFlow and PyTorch
- Familiarity with key concepts and techniques used in generative models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and flow-based models.
- Strong programming skills in languages such as Python, along with experience working with popular deep learning frameworks like PyTorch and TensorFlow.
- Understanding of Graph Database and/or Vector Database along with knowledge of cloud services (e.g., AWS, Azure).
- Experience with deploying AI models in production environments.
- Familiarity with domain-specific applications of generative AI
- Excellent communication and problem-solving skills
