Senior AI/ML Engineer

𝗝𝗼𝗯 𝗧𝗶𝘁𝗹𝗲: Senior AI/ML Engineer 𝗝𝗼𝗯 𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Chennai, Tamil Nadu, India 𝗪𝗼𝗿𝗸 𝗠𝗼𝗱𝗲: Hybrid (WFO: Tuesday, Wednesday & Thursday) 𝗝𝗼𝗯 𝗧𝘆𝗽𝗲: Full-Time 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗱: 8+ Years Overall Experience; 3+ Years in AI/ML Engineering 𝗧𝗵𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 We are seeking an experienced Senior AI/ML Engineer to design, develop, and deploy scalable machine learning solutions that power intelligent products and data-driven decision-making. In this role, you will be responsible for building production-grade ML systems, developing automated model training and deployment pipelines, and integrating machine learning capabilities into large-scale applications. You will collaborate closely with data scientists, software engineers, and platform teams to deliver reliable, scalable, and maintainable AI solutions. This position is ideal for engineers who enjoy solving complex problems, working with cloud-native machine learning platforms, and driving AI innovation at scale. 𝗥𝗼𝗹𝗲𝘀 & 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀

• Design, develop, and deploy machine learning models for predictive analytics, optimization, and intelligent automation use cases.
• Build scalable and production-ready ML workflows using Amazon SageMaker.
• Develop end-to-end machine learning pipelines, including: o Data preparation o Feature engineering o Model training o Model validation o Model deployment
• Automate model lifecycle management using Amazon SageMaker Pipelines and MLOps best practices.
• Integrate machine learning models into backend services, APIs, and distributed systems.
• Monitor model performance, detect model drift, and implement retraining strategies to maintain accuracy and reliability.
• Collaborate with software engineering and platform teams to ensure ML systems are scalable, secure, and operationally efficient.
• Improve deployment, monitoring, and observability of machine learning infrastructure.
• Participate in architecture discussions and contribute to AI/ML platform design decisions.
• Mentor engineers and share best practices across machine learning engineering initiatives. 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 & 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 𝗠𝘂𝘀𝘁-𝗛𝗮𝘃𝗲 𝗤𝘂𝗮𝗹𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, Engineering, or a related field.
• 8+ years of overall software engineering experience.
• 3+ years of hands-on experience designing and deploying machine learning models in production environments.
• Strong programming proficiency in Python.
• Experience with machine learning frameworks and libraries, including: o Scikit-learn o XGBoost o TensorFlow o LightGBM
• Hands-on experience deploying and managing machine learning workloads using Amazon SageMaker or equivalent cloud-based ML platforms.
• Strong understanding of: o Feature engineering o Model selection o Model evaluation o Hyperparameter tuning o Model deployment strategies
• Experience working with cloud-native architectures and large-scale data systems.
• Strong analytical, debugging, and problem-solving skills.
• Ability to collaborate effectively with cross-functional engineering and product teams. 𝗣𝗿𝗲𝗳𝗲𝗿𝗿𝗲𝗱 𝗤𝘂𝗮𝗹𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀
• Experience building production-grade ML pipelines and automated model training workflows.
• Exposure to MLOps practices and ML lifecycle management tools.
• Experience with distributed data processing platforms and large-scale data ecosystems.
• Knowledge of advanced AI techniques, including: o Deep Learning o Reinforcement Learning o Optimization Models
• Experience implementing monitoring, observability, and governance frameworks for machine learning systems.
• Familiarity with cloud-based data engineering and modern deployment architectures.
• Experience mentoring engineers and leading technical initiatives. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴
• Python 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀
• Scikit-learn
• XGBoost
• TensorFlow
• LightGBM 𝗖𝗹𝗼𝘂𝗱 & 𝗠𝗟𝗢𝗽𝘀
• Amazon SageMaker
• SageMaker Pipelines
• Cloud-Native ML Deployment
• Model Lifecycle Management 𝗔𝗜/𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
• Predictive Modeling
• Feature Engineering
• Model Evaluation
• Model Deployment
• Model Monitoring
• Model Drift Detection
• Automated Retraining 𝗗𝗮𝘁𝗮 & 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴
• Large-Scale Data Systems
• Distributed Computing
• Production ML Pipelines
• Data Processing Workflows