onsite
AI/ML Engineer - ApplogiQ
ML Engineer
Design, build, and deploy scalable AI/ML models and pipelines for enterprise applications, leveraging Python, TensorFlow/PyTorch, and cloud services such as AWS.
About the role
Key Responsibilities
- Develop, train, and deploy machine learning models for enterprise use cases, including NLP, computer vision, and predictive analytics.
- Build end‑to‑end, scalable ML pipelines that handle data ingestion, preprocessing, feature engineering, and model serving.
- Integrate AI services with business applications using FastAPI or Flask, exposing models via RESTful APIs.
- Containerize applications with Docker and orchestrate deployments on AWS cloud infrastructure.
- Monitor model performance in production, perform continuous optimization, and implement version control for models and code.
- Collaborate with data engineers, product managers, and domain experts to translate business requirements into AI solutions.
Requirements
- 2–8 years of hands‑on experience developing AI/ML solutions in Python.
- Proficiency with machine‑learning libraries such as TensorFlow, PyTorch, Scikit‑learn, and data manipulation tools like Pandas.
- Experience building and deploying APIs with FastAPI or Flask and containerizing applications using Docker.
- Solid understanding of cloud platforms, preferably AWS, for model hosting and scaling.
- Strong problem‑solving skills, ability to work cross‑functionally, and a track record of delivering production‑ready AI models.
Skills
pythonmachine learningtensorflowpytorchpandasfastapiaws