Applied AIML Lead-Python & Agentic AI

b Overview /b p As a Machine Learning Lead Software Engineer, you will guide an agile team to design and deliver trusted AI/ML products that scale within a secure infrastructure. You will work closely with product managers, data scientists, and ML engineers to deploy state-of-the-art AI/ML/LLM/GenAI solutions that meet business objectives. You mentor ML and MLOps engineers and own automated model deployment pipelines for reliability and efficiency. You will optimize generative NLP models, monitor performance in real time, and translate complex AI capabilities for technical and non-technical audiences. This role offers the chance to shape enterprise AI capabilities in a leading financial services institution /p b Responsibilities /b ul li Collaborate with product managers, data scientists, ML engineers, and stakeholders to understand requirements and priorities /li li Design, develop, and deploy AI/ML/LLM/GenAI solutions aligned with business goals /li li Manage and mentor a team of ML and MLOps engineers /li li Develop and maintain automated deployment pipelines for scalable, reliable model delivery /li li Implement optimization strategies to fine-tune generative models for NLP use cases /li li Evaluate and iterate on model architectures to improve NLP performance /li li Implement real-time monitoring to ensure model reliability /li li Communicate AI/ML capabilities and results to technical and non-technical audiences /li li Stay current with AI/ML/LLM/GenAI advancements and leverage external APIs for enhanced functionality /li /ul b Key requirements /b ul li Bachelors or Masters degree in Computer Science, Engineering, or related field /li li Proven experience in applied AI/ML engineering with production deployments /li li Proficiency in Python for model development and OpenAI API integration /li li Experience with TensorFlow, PyTorch, Scikit-learn, and OpenAI API /li li Experience with cloud platforms (AWS, Azure, or GCP), Docker, Kubernetes, and microservices /li li Solid understanding of statistics and ML fundamentals (classification, regression, time series, deep learning, reinforcement learning) and generative architectures (GANs, VAEs) /li li Ability to address AI/ML/LLM/GenAI challenges and optimize models for NLP /li li Strong collaboration skills for cross-functional work /li /ul ul li collaboration /li li clear communication /li li stakeholder engagement /li li Python /li li TensorFlow /li li PyTorch /li /ul