Sr. AI ML Engineer

Job Description: Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area This is a unique opportunity apply your skills and have a direct impact on global business. You will be building production-grade AI Agentic workflows ML services developing end-to-end AI/ML pipelines and collaborating to develop large-scale data modeling experiments. Your expertise in Python PySpark Knowledge Graphs RAG Vector stores DL frameworks like TensorFlow and MLOps will be crucial in this role.

As a senior software engineer with python Lang chain Lang graph ai/ml engineering experience. Someone who has built some agentic applications.

Job responsibilities

• Work closely with product managers data scientists ML engineers and other stakeholders to understand requirements and prioritize use cases.
• Design develop and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
• Develop and maintain automated pipelines for model deployment ensuring scalability reliability and efficiency.
• Implement optimization strategies to fine-tune generative models for specific NLP use cases ensuring high-quality outputs in summarization and text generation.
• Conduct thorough evaluations of generative models (e.g. GPT-4.1) iterate on model architectures and implement improvements to enhance overall performance in NLP applications.
• Implement monitoring mechanisms to track model performance in real-time and ensure model reliability.
• Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences.
• Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research implement cutting-edge techniques and leverage external APIs for enhanced functionality.

Required qualifications capabilities and skills

• Bachelors or Masters degree in Computer Science Engineering or a related field
• 6-9 years of demonstrated experience in applied AI/ML engineering with a track record of developing and deploying business critical machine learning models in production.
• Proficiency in programming languages like Python for model development experimentation and integration with OpenAI API.
• Experience with machine learning frameworks libraries and APIs such as TensorFlow PyTorch Scikit-learn and OpenAI API.
• Experience with cloud computing platforms (e.g. AWS Azure or Google Cloud Platform) containerization technologies (e.g. Docker and Kubernetes) and microservices design implementation and performance optimization.
• Solid understanding of fundamentals of statistics machine learning (e.g. classification regression time series deep learning reinforcement learning) and generative model architectures particularly GANs VAEs.
• Ability to identify and address AI/ML/LLM/GenAI challenges implement optimizations and fine-tune models for optimal performance in NLP applications.
• Strong collaboration skills to work effectively with cross-functional teams communicate complex concepts and contribute to interdisciplinary projects.
• A portfolio showcasing successful applications of generative models in NLP projects including examples of utilizing OpenAI APIs for prompt engineering.