AI/ML Engineer

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.

Originally posted on Himalayas