Avaali Solutions - Ai ML Engineer

Role Overview: The AI/ML Engineer will play a critical role in building and operationalizing machine learning models across a range of business use cases. This person will work closely with data scientists, software engineers, and business stakeholders to develop intelligent solutions that enhance our products and internal processes. Key Responsibilities: 1. Model Development: o Design, build, train, and optimize machine learning and deep learning models. o Research and prototype new algorithms and approaches. 2. Data Engineering Support: o Work with large structured and unstructured datasets; perform data cleaning, transformation, and feature engineering. 3. Productionization: o Deploy models to production environments, ensuring scalability, performance, and maintainability. o Build APIs, pipelines, and automation to integrate models into products and workflows. 4. Collaboration: o Collaborate cross-functionally with product managers, data scientists, and software engineers. o Translate business needs into technical solutions with clear impact. 5. Monitoring and Maintenance: o Monitor model performance post-deployment and implement retraining strategies as needed. 6. Innovation: o Stay current with the latest research in AI/ML and suggest improvements to existing solutions. Required Skills & Qualifications: 1. Bachelors or Master’s degree in Computer Science, Data Science, Engineering, Mathematics, or a related field. 2. Strong programming skills in Python and experience with ML libraries (TensorFlow, PyTorch, scikit-learn, etc.). 3. Proficiency with data handling tools like SQL, Pandas, Spark, etc. 4. Experience with cloud platforms (AWS, Azure, or GCP) and deploying models at scale. 5. Knowledge of MLOps practices (CI/CD for ML, model versioning, monitoring tools). 6. Familiarity with REST APIs, microservices, and containerization (Docker, Kubernetes). 7. Strong problem-solving skills and ability to work independently. Preferred Qualifications: 1. Experience with Generative AI models (e.g., LLMs, diffusion models). 2. Knowledge of NLP, Computer Vision, or Time-Series Analysis. 3. Exposure to ML frameworks like MLflow, Kubeflow, or SageMaker. 4. Understanding of AI governance, fairness, and ethical AI practices.