Job Title: AI/ML Engineer
Department: IT Services
Reports To: IT Project Manager
Job Overview:
The AI/ML Engineer plays a critical role in designing, developing, and deploying machine learning models and AI-driven solutions to support strategic business initiatives. The role involves collaborating with cross-functional teams, including software engineering, data analytics, product development, and business stakeholders, to drive intelligent automation, data-driven decision-making, and advanced analytics capabilities.
The ideal candidate will have 3 to 5 years of experience in AI/ML model development, with a strong foundation in machine learning algorithms, data preprocessing, and deployment pipelines. Experience with Python, TensorFlow/PyTorch, and cloud-based ML services is essential.
Responsibilities:
1. Model Development and Optimization
- Design, build, and deploy ML models for classification, regression, NLP, computer vision, or time-series forecasting.
- Select appropriate algorithms and techniques based on business needs and data characteristics.
- Continuously monitor and improve model performance using metrics and feedback loops.
2. Data Preparation and Feature Engineering
- Clean, preprocess, and transform structured and unstructured datasets for training and inference.
- Engineer and select relevant features to improve model accuracy and generalizability.
- Collaborate with data engineers to ensure data quality and accessibility.
3. Model Deployment and MLOps
- Package and deploy models using tools like Docker, Flask/FastAPI, and Kubernetes.
- Implement CI/CD pipelines for ML using platforms like MLflow, Airflow, or Kubeflow.
- Monitor deployed models for drift, latency, and performance in production environments.
4. AI Solutions and Use Case Implementation
- Work with business stakeholders to translate real-world problems into AI/ML use cases.
- Prototype and test AI-driven solutions (e.g., recommendation engines, chatbots, fraud detection).
- Contribute to proof-of-concept projects and assist in scaling successful models to production.
5. Research and Innovation
- Stay updated with the latest research, frameworks, and tools in machine learning and AI.
- Experiment with cutting-edge models (e.g., LLMs, transformers, generative AI) and assess their viability.
- Promote innovation by recommending and implementing modern AI strategies.
6. Cross-functional Collaboration
- Collaborate with software developers, DevOps, data analysts, and domain experts for end-to-end solution delivery.
- Translate technical insights into business value through clear documentation and presentations.
7. Documentation and Best Practices
- Maintain comprehensive documentation for models, experiments, and pipelines.
- Ensure reproducibility, scalability, and compliance with data governance policies.
Requirements:
Experience:
- 3–5 years of hands-on experience in machine learning model development and deployment.
- Proven track record of solving real-world problems using supervised, unsupervised, or deep learning methods.
Technical Skills:
Strong knowledge of:
- Python and ML libraries (scikit-learn, pandas, NumPy, TensorFlow/PyTorch)
- Model evaluation, hyperparameter tuning, and pipeline automation
- REST APIs for model serving and integration
Familiarity with:
- MLOps tools (MLflow, Airflow, DVC, Docker, Kubernetes)
- Cloud ML services (AWS SageMaker, Azure ML, GCP AI Platform)
- NLP or computer vision frameworks (e.g., Hugging Face, OpenCV)
Soft Skills:
- Strong analytical and problem-solving abilities.
- Excellent communication skills, both verbal and written.
- Ability to work independently and within cross-functional teams.
- Curiosity, adaptability, and willingness to learn continuously.