AI/ML Engineer

Uvation; India; On-site; INR 900,000 - 1,500,000; Full time

Job summary

Uvation is seeking an AI/ML Engineer to design, develop, and deploy ML models across classification, NLP, and computer vision tasks. You will work with Python, ML libraries, and cloud ML services, ensuring robust data pipelines and scalable deployment through Docker and Kubernetes.

You will collaborate with software, data analytics, and product teams to translate business problems into AI-enabled solutions, implement MLOps practices, and stay updated with the latest ML research and tools.

Qualifications

• 3–5 years of hands-on experience in AI/ML model development and deployment.

Responsibilities

• Design, build, and deploy ML models for classification, regression, NLP, CV, or time-series.
• Select algorithms based on data characteristics and business needs.
• Monitor and improve model performance with metrics and feedback loops.
• Prepare and preprocess structured and unstructured data for training and inference.
• Package and deploy models using Docker, Flask/FastAPI, and Kubernetes.
• Implement CI/CD pipelines for ML using MLflow, Airflow, Kubeflow.
• Collaborate with cross-functional teams to translate business problems into AI solutions.

Skills

Python

ML libraries

Model evaluation

REST APIs

MLOps

Cloud ML

Tools

Docker

Kubernetes

MLflow

Airflow

Kubeflow

DVC

Job description

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.