Senior AI/Machine Learning Engineer

Summary

Build and deploy production-grade AI/ML models, including LLMs with RAG and AI agents, using Python, PyTorch/TensorFlow, and cloud MLOps tools.

Design, develop, deploy, and maintain scalable AI/ML solutions for production environments.
Build, fine-tune, and optimize machine learning, deep learning, and Generative AI models to solve complex business problems.
Develop and implement Large Language Model (LLM) applications using prompt engineering, Retrieval-Augmented Generation (RAG), AI agents, and vector databases.
Design and optimize data pipelines, feature engineering workflows, and model training processes.
Deploy and manage ML models using MLOps best practices, ensuring scalability, reliability, and performance.
Collaborate with product managers, software engineers, data engineers, and business stakeholders to translate business requirements into AI-driven solutions.
Evaluate model performance, conduct A/B testing, monitor production models, and implement continuous improvements.
Develop APIs and integrate AI/ML models into enterprise applications and cloud-based systems.
Ensure AI solutions adhere to security, governance, explainability, and responsible AI standards.
Mentor junior engineers, conduct code reviews, and promote engineering best practices.
Stay current with advancements in AI, machine learning, and Generative AI technologies and recommend innovative solutions.

5 to 10 years of experience in AI, Machine Learning, Data Science, or related fields.
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Mathematics, Statistics, or a related discipline.
Strong hands-on experience with Python and AI/ML frameworks such as PyTorch, TensorFlow, Scikit-learn, and Hugging Face.
Proven experience building and deploying production-grade machine learning and Generative AI applications.
Hands-on experience with LLMs, Prompt Engineering, RAG, AI Agents, LangChain, LlamaIndex, and vector databases.
Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform and MLOps tools including Docker, Kubernetes, MLflow, or Kubeflow.
Strong understanding of machine learning algorithms, deep learning, NLP, and model optimization techniques.
Experience developing REST APIs, microservices, and scalable distributed systems.
Excellent analytical, problem-solving, communication, and stakeholder management skills.
Ability to work independently in a fast-paced, collaborative environment and lead technical initiatives.
Experience mentoring team members and driving technical excellence is highly desirable.