AI Engineer

Job Description

Key Responsibilities Statistical Analysis & Modeling: Conduct advanced statistical modeling, including regression, hypothesis testing, and time series forecasting, while focusing on speech and language data. Machine Learning & AI: Oversee the training, evaluation, and fine-tuning of agents and NLP models (e.g., transformers, GPT, T5). Deep Learning: Work with deep learning frameworks like TensorFlow, PyTorch, and Hugging Face Transformers to build and scale models that process and generate data. Big Data & Cloud Technologies: Leverage cloud platforms such as GCP for scalable data storage and computation, and work with Spark, Hadoop for distributed processing of large datasets involved in speech modeling. Data Engineering & Pipelines: Focusing on ETL workflows that extract, transform, and load text data, ensuring robust pipelines for training and fine-tuning models. Programming & Scripting: Proficient in Python, SQL for handling and analyzing voice data. Use libraries such as NLTK, spaCy, Pandas, Numpy and for NLP tasks. Experience with Shell scripting or Excel VBA for automating data extraction and reporting. Natural Language Processing (NLP): Develop and implement NLP techniques to improve agents' understanding, dialogue generation, and context management, ensuring that AI models respond to human speech in a natural and effective manner. Team Leadership: Mentor and guide junior team, promoting best practices in AI model development and technology innovation. Lead cross-functional initiatives and manage large, complex projects. Business Acumen & Leadership Domain Expertise: Experience working with Testing data, NLP, and generative AI models in industries like HealthCare, Telecom and IT Services. Translate business problems into actionable data-driven AI solutions. Decision Science: Build advanced decision-support tools, including dashboards for real-time model performance, user engagement, and ROI metrics. Ethical AI & Fairness: Ensure ethical usage of AI, with a focus on fairness, privacy, and inclusivity in model responses and user interactions. Soft Skills & Mindset Curiosity & Learning: Passion for staying up-to-date with advancements in generative AI models, and NLP. Willing to experiment with new approaches, technologies, and research papers. Storytelling: Ability to present complex findings from and NLP models to both technical and non-technical stakeholders in a clear, understandable manner.