Sr. AI Developer

What You'll Do As a Senior AI Engineer, you will design, develop, deploy, and scale enterprise-grade AI and Machine Learning solutions that drive intelligent automation and business transformation. Design and develop end-to-end AI/ML solutions for real-world business problems. Build predictive models, classification systems, recommendation engines, and intelligent automation solutions. Apply supervised, unsupervised, and deep learning techniques based on business requirements. Develop and optimize data pipelines, feature engineering, and model training workflows. Evaluate and improve model accuracy, scalability, robustness, and business impact. Build and deploy scalable model-serving APIs using Python, FastAPI, Flask, or similar frameworks. Deploy AI solutions using Docker, microservices, CI/CD pipelines, and cloud-native architectures. Monitor model performance, data quality, model drift, and retraining requirements. Troubleshoot production issues across AI models, APIs, and data pipelines. Apply MLOps practices for model versioning, deployment, monitoring, and lifecycle management. Implement Responsible AI practices, including explainability, bias mitigation, governance, and security. Collaborate with Data Scientists, Data Engineers, Software Engineers, Product Managers, and business stakeholders. Mentor junior engineers and contribute to architecture, code reviews, and technical best practices. Stay current with emerging AI, Generative AI, LLM, and cloud technologies. What We Seek In You 5–8 years of experience in AI Engineering, Machine Learning, Applied AI, or related fields. Strong hands-on expertise in Python and SQL. Machine Learning and Deep Learning Supervised and Unsupervised Learning Feature Engineering and Model Evaluation Model Optimization and Validation Hands-on experience with TensorFlow, PyTorch, Keras, Scikit-learn, NumPy, and Pandas. Experience building and deploying production-grade AI/ML applications. Strong experience with FastAPI, Flask, REST APIs, and Microservices. Hands-on experience with Docker, Git, CI/CD, and cloud-native deployments. Experience with at least one major cloud platform: Azure, AWS, or GCP. Experience with AI/ML platforms such as Azure Machine Learning, AWS SageMaker, or Vertex AI. Strong understanding of MLOps, model monitoring, model drift, and retraining workflows. Excellent problem-solving, debugging, communication, and stakeholder management skills. Ability to translate complex business challenges into scalable AI-powered solutions. Preferred Qualifications Experience with Generative AI, Large Language Models (LLMs), RAG, and NLP. Exposure to Computer Vision and advanced AI use cases. Experience with MLOps tools such as MLflow, Kubeflow, or Apache Airflow. Familiarity with Apache Spark and Big Data technologies. Understanding of Responsible AI, Explainable AI (XAI), AI Governance, and AI Ethics. Experience designing scalable, secure, and cloud-native…