Senior AI/ML Engineer

We are seeking an AI/ML Engineer with hands-on experience in building, deploying, and scaling AI/ML solutions. The ideal candidate will have strong programming skills, a passion for developing intelligent solutions, and familiarity with the latest AI/ML frameworks and tools. This role will involve close collaboration with client and software engineers to implement machine learning models and deploy them into production environments.
Required Experience: 3-4 Years

Job Location: Ahmedabad

Required Educational Qualifications: Bachelors or Masters degree in Computer Science, Data Science, AI, Machine Learning, or a related field

Key Responsibilities:

• Independently design, develop, and deploy machine learning models and AI algorithms tailored to business requirements.
• Solve complex technical challenges by researching and implementing innovative AI/ML solutions.
• Build and maintain Integration (e.g. APIs) to expose machine learning models and support integration with other systems.
• Collaborate with cross-functional teams to understand project requirements and deliver AI/ML-driven insights and solutions.
• Conduct data preprocessing, feature engineering, and optimize datasets for model training and inference.
• Implement, monitor, and continuously improve model performance in production environments, focusing on scalability and efficiency.
• Manage model deployment, monitoring, and scaling using tools like Docker, Kubernetes, and cloud services.
• Develop integration strategies to ensure smooth communication between APIs and other systems, and troubleshoot integration issues as they arise.
• Create and maintain comprehensive documentation for AI/ML projects, covering model parameters, API endpoints, and integration workflows.
• Stay updated with emerging trends and technologies in AI/ML and actively contribute to the teams knowledge base.

Must-Have Skills:

• Programming Skills: Proficiency in Python (must have), R, or similar languages commonly used in ML/AI development.
• Machine Learning Frameworks: Hands-on experience with TensorFlow, PyTorch, scikit-learn, or similar ML libraries.
• Data Handling: Strong knowledge of data preprocessing, data cleaning, and feature engineering.
• Model Deployment: Familiarity with model deployment practices, including using Docker, Kubernetes, or cloud platforms (AWS, Azure, Google Cloud).
• Statistical Knowledge: Strong understanding of statistical methods, probability, and data-driven decision-making processes.
• SQL and NoSQL Databases: Proficient in querying databases to extract relevant data for ML projects.
• MLOps Tools: Experience with ML lifecycle management tools such as MLflow, Kubeflow.
• Natural Language Processing (NLP): Experience with NLP frameworks (e.g., spaCy, NLTK, BERT) for language-based AI solutions.
• Computer Vision: Familiarity with image processing and computer vision techniques.
• Cloud ML Services: Experience with managed ML services like AWS SageMaker, Azure Machine Learning, or Google Cloud AI Platform.
• Experience with Agile Development: Familiarity with agile workflows, including experience working with DevOps or CI/CD pipelines.

Good to Have Skills:
Big Data Technologies: Exposure to Spark, Hadoop, or similar big data processing tools.