Senior AI / ML Solutions Engineer

We are seeking a highly skilled Senior AI/ML Solutions Engineer with 4-5 years of experience to design, develop, and deploy state-of-the-art AI and machine learning solutions. The ideal candidate will play a critical role in shaping our AI/ML strategy, building robust models, and ensuring seamless integration with business applications.

• Solution Development: Design and implement scalable AI/ML solutions to solve complex business challenges.

• Data Analysis: Preprocess, analyze, and interpret large datasets to extract actionable insights.

• Model Building: Develop, train, and fine-tune machine learning models using frameworks like TensorFlow, PyTorch, or scikit-learn.

• Integration: Work closely with software engineering teams to integrate ML models into production systems.

• Performance Optimization: Continuously monitor and optimize model performance to ensure accuracy and efficiency.

• Collaboration: Collaborate with cross-functional teams, including data scientists, product managers, and stakeholders, to align solutions with business goals.

• Research & Innovation: Stay updated on the latest AI/ML trends and technologies to propose innovative approaches.

• Documentation: Create detailed documentation for processes, models, and systems to ensure clarity and knowledge sharing.

• Bachelor s or Master s degree in Computer Science, Data Science, Engineering, or a related field.

• 4-5 years of experience in designing and implementing AI/ML solutions.

• Proficiency in programming languages such as Python, R, Java or JavaScript.

• Strong experience with machine learning frameworks (e.g., TensorFlow, PyTorch, Keras).

• Hands-on experience with cloud platforms like AWS, Azure, or Google Cloud for AI/ML deployments.

• Solid understanding of data structures, algorithms, and statistical methods.

• Expertise in data preprocessing and feature engineering techniques.

• Strong problem-solving skills and the ability to work independently or in a team.

• Experience with version control systems like Git.

• Familiarity with CI/CD pipelines for ML model deployment.

• Experience with Gen AI, natural language processing (NLP), computer vision, or reinforcement learning.

• Knowledge of containerization and orchestration tools like Docker and Kubernetes.

• Familiarity with big data tools such as Spark, Hadoop, or Apache Kafka.

• Strong understanding of MLOps practices and principles.