Artificial Intelligence Engineer

Job Summary
We are looking for a highly skilled and experienced AI Engineer with strong expertise in Machine Learning, Generative AI, MLOps, and CI/CD automation using Jenkins. The ideal candidate will be responsible for designing, developing, deploying, and maintaining scalable AI/ML solutions while implementing robust automation pipelines for model training, testing, deployment, and monitoring.
This role requires close collaboration with Data Scientists, DevOps Engineers, Software Developers, and Cloud teams to operationalize AI models in production environments.
Key Responsibilities
AI/ML Development
Design, develop, and deploy machine learning and AI solutions for business applications.
Build scalable AI systems using Python and modern AI/ML frameworks.
Develop and optimize NLP, Computer Vision, Recommendation Systems, Predictive Analytics, or Generative AI applications.
Work with LLMs, prompt engineering, embeddings, vector databases, and RAG architectures where applicable.
Fine-tune and evaluate machine learning models for performance, scalability, and accuracy.
MLOps & Automation
Build and maintain end-to-end MLOps pipelines for model training, validation, deployment, and monitoring.
Implement CI/CD pipelines using Jenkins for automated build, testing, and deployment of AI applications.
Automate model lifecycle management including versioning, retraining, rollback, and monitoring.
Integrate Jenkins with Git, Docker, Kubernetes, Terraform, and cloud-native deployment tools.
Ensure reproducibility and reliability of AI workflows.
Cloud & Infrastructure
Deploy AI/ML workloads on cloud platforms such as AWS, Azure, or GCP.
Manage containerized AI applications using Docker and Kubernetes.
Work with infrastructure-as-code and orchestration tools.
Optimize GPU/compute resource utilization for training and inference workloads.
Data Engineering & Integration
Collaborate with data teams to prepare and process structured/unstructured datasets.
Build data pipelines and integrate AI services with enterprise systems and APIs.
Ensure data quality, governance, and security compliance.
Monitoring & Production Support
Monitor production AI systems for model drift, performance degradation, and operational issues.
Implement logging, observability, alerting, and performance dashboards.
Troubleshoot deployment and infrastructure issues in production environments.
Collaboration & Leadership
Mentor junior engineers and guide best practices in AI engineering and DevOps.
Collaborate with cross-functional stakeholders to translate business requirements into technical solutions.
Participate in architecture reviews, sprint planning, and technical decision-making.
Required Skills & Qualifications
Technical Skills
5+ years of experience in AI/ML Engineering, Software Engineering, or MLOps.
Strong programming skills in Python.
Hands-on experience with Jenkins CI/CD pipelines.
Experience with machine learning frameworks:
TensorFlow
PyTorch
Scikit-learn
Hugging Face Transformers
Experience with Generative AI and LLM ecosystems.
Strong understanding of MLOps principles and deployment workflows.
Experience with:
Docker
Kubernetes
Git/GitHub/GitLab
REST APIs
Cloud platform expertise in AWS, Azure, or GCP.
Experience with databases:
SQL/NoSQL
Vector databases (preferred)
Familiarity with monitoring tools such as Prometheus, Grafana, ELK, or CloudWatch.
Jenkins-Specific Expertise
Designing declarative and scripted Jenkins pipelines.
Integrating Jenkins with automated testing and deployment frameworks.
Managing Jenkins agents/nodes and plugin ecosystem.
Implementing secure credential management and deployment automation.
Experience integrating Jenkins with Kubernetes and Docker-based deployments.