Lead Software Engineer- NLP, Gen AI

: AI-focused Software Development position responsible for the architecture, implementation, design, and deployment of algorithms and Python based applications to help fulfill our Research & Consulting Delivery strategy. What you’ll do: • Establish methodologies for quickly rolling out new data analysis capabilities for standalone data-driven products and services to support our associates using AI, ML, and LLMs. • Lead architecture and technical design discussions to ensure data science solutions align with the organization's strategic objectives. • Continuously improve models through experimentation and optimization techniques. • Analyze unstructured text data to discover insights and patterns using advanced data science techniques, including machine learning and natural language processing • Use a combination and quantitative (science) and qualitative (art) methodologies to prioritize AI initiatives. • Stay on top of fast-moving AI/ML models and technologies. Understand and follow disruptive data science solutions. Evangelize new technologies and drive their adoption. • Create Data Science packages and APIs for use across the organization • Mentor junior members of the team on effective technical practices and non-technical acumen • Be accountable for the scalability, stability, and business adoption of data science solutions What you’ll need: 7-9 years of experience in algorithms and statistics and experience in data mining, machine learning, deep learning and natural language processing. Ability to provision packages and APIs for production data science code. Must have: •

qualification as graduate or postgraduate degree in Engineering or Data Science is required. • Experience in Machine learning models and techniques like NLP, BERT, Transformers, Deep learning. • Experience using LLMs (e.g., OpenAI, Cohere, Anthropic, Llama, etc) to deliver business outcomes. • Experience building Generative AI and Agentic AI solutions using LangChain, LangGraph, Langfuse, RAG, and prompt engineering. • Stay on top of fast-moving AI/ML models and technologies. Understand and follow disruptive data science solutions. Evangelize new technologies and drive their adoption. • Demonstrated ability to translate quantitative analysis into actionable business strategies. • Ability to work collaboratively across business, data science and technical stakeholders.