Senior Python AI/ML Engineer
Overview:
In this role, you design, develop, and deploy enterprise AI/ML and Generative AI solutions. You will build end-to-end data pipelines, integrate LLMs and RAG approaches, and ship production-quality Python backends and APIs. The role spans data engineering, MLOps, and cloud deployment to deliver scalable AI capabilities. You will work closely with cross-functional teams to improve model performance, reliability, and security, shaping how the organization leverages AI at scale.
Responsibilities:
- Design, develop, test, and deploy ML/Generative AI solutions for enterprise use cases
- Build end-to-end AI/ML pipelines including data ingestion, feature engineering, model training, evaluation, deployment, and monitoring
- Develop Retrieval-Augmented Generation (RAG) solutions and integrate LLMs into enterprise apps
- Develop scalable backend services, microservices, and RESTful APIs using PythonCreate reusable frameworks, libraries, and integration components
- Write production-quality, secure, maintainable code with modern engineering standards
- Implement unit, integration, regression, and performance testing
- Design data pipelines for structured and unstructured data processing
- Integrate AI/ML models with internal and external systems via APIs and event-driven patterns
- Collaborate with data engineering teams to support data quality, governance, and security
- Build and maintain CI/CD pipelines for application, infra, and model deployments
- Automate build, test, code-quality checks, security scanning, deployment, and releasesMLOps practices including model versioning, experiment tracking, monitoring, drift detection, and retraining
- Utilize tools such as Jenkins, GitHub Actions, Azure DevOps, Bitbucket Pipelines, Docker, Kubernetes
- Design and deploy cloud-native solutions on AWS, Azure, or GCPKey requirements 8-12 years of software engineering experience with substantial Python development
- Production experience designing, deploying, and supporting AI/ML solutions
- Strong understanding of ML algorithms, model lifecycle management, and MLOpsExperience developing RESTful APIs, microservices, and system integrations
- Hands-on experience with AWS, Azure, or GCPExperience creating and maintaining CI/CD pipelines and applying DevOps practices
- Experience with Docker, Kubernetes, SQL, Git, automated testing, debugging, and production support
- Strong analytical skills
- Effective communication and stakeholder management
- Problem-solving oriented
- PythonRESTful APIsMicroservices