An experienced AI Engineer who brings together the best of data analytics, cloud computing, and scalable AI application development. You will be responsible for designing, developing, and deploying AI solutions that leverage real-time data pipelines, APIs, and containerized microservices.
Key Responsibilities :
- Develop, deploy, and maintain AI / ML models and pipelines in production environments.
- Build and manage FastAPI-based APIs to serve AI models and analytics results.
- Design scalable and secure cloud-based architectures for AI services (AWS / Azure / GCP).
- Containerize applications using Docker and orchestrate with Kubernetes.
- Collaborate with data scientists, backend engineers, and DevOps teams to integrate models into applications.
- Optimize data ingestion, preprocessing, and model inference pipelines for performance and reliability.
- Monitor and improve model accuracy and system performance post-deployment.
Required Skills & Qualifications :
Bachelor's or Masters degree in Computer Science, AI / ML, Data Engineering, or related field.3+ years of experience in AI / ML engineering or backend data systems.Proficient in Python with experience in FastAPI, NumPy, pandas, and ML libraries (scikit-learn, PyTorch, or TensorFlow).Strong understanding of cloud services (AWS / GCP / Azure) and deployment best practices.Hands-on experience with Docker and Kubernetes for scalable service orchestration.Solid background in data analytics, including ETL, big data processing, and model interpretation.Familiarity with CI / CD tools and MLOps frameworks is a plus.Preferred candidate :
Experience with real-time data pipelines (Kafka, Spark Streaming, etc.)Knowledge of database systems (SQL and NoSQL)Exposure to monitoring tools like Prometheus, Grafana, or ELK stackPrior experience working with edge AI or distributed ML environments(ref : hirist.tech)