Machine Learning Engineer

Overview

Job Summary:
We are looking for a highly motivated and skilled Machine Learning Engineer with 1–3 years of experience and a strong interest in Generative AI technologies. The ideal candidate will contribute to the full lifecycle of Generative AI models — from training and optimization to deployment on AWS and inference API development. You will collaborate with a team of engineers and researchers to build cutting-edge AI-powered solutions that have real-world impact.

Key Responsibilities:
1. Generative AI Model Development
Design, develop, and implement Generative AI models for applications such as text, image, and code generation.
Work with large language models (LLMs) or other generative models using frameworks like TensorFlow, PyTorch, and Hugging Face Transformers.
Experiment with model architectures, training techniques, and hyperparameter tuning to optimize performance.
2. Model Optimization & Efficiency
Apply techniques such as quantization, pruning, and distillation to improve inference speed and reduce resource consumption.
Profile and analyze model performance to identify and eliminate bottlenecks.
3. Cloud Deployment on AWS
Deploy AI models to AWS using services such as SageMaker, EC2, ECS, or Lambda.
Develop scalable and reliable inference pipelines.
Use AWS tools for data storage, model management, and monitoring.
4. Inference API Development
Design and develop efficient, secure, and scalable RESTful or gRPC APIs to serve deployed AI models.
Ensure high availability, maintainability, and performance of APIs.
5. Troubleshooting & Debugging
Diagnose and resolve issues related to model performance, deployment, and API functionality.
Implement monitoring and logging systems to proactively manage issues.
6. Model Retraining & Continuous Improvement
Implement strategies for continuous learning by retraining models with new data and user feedback.
Monitor production model performance and trigger retraining workflows as needed.
7. Collaboration & Communication
Work closely with ML engineers, data scientists, and software developers to drive project goals.
Communicate progress, insights, and challenges effectively with stakeholders.
8. Documentation
Create comprehensive documentation for models, training workflows, deployment processes, and APIs.

Required Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, or a related field.
• 1–3 years of hands-on experience in machine learning model development and deployment.
• Sound understanding of Generative AI concepts (e.g., Transformers, GANs, Diffusion Models).
• Experience with at least one deep learning framework: TensorFlow or PyTorch.
• Proficiency in Python.
• Solid knowledge of ML algorithms, data preprocessing, and evaluation metrics.
• Proven experience in deploying models on AWS.
• Strong skills in developing and consuming RESTful or gRPC APIs.
• Familiarity with Git or other version control systems.
• Excellent problem-solving, communication, and teamwork skills.

Preferred Qualifications:

• Experience working with LLMs and the Hugging Face Transformers library.
• Knowledge of MLOps practices and related tools.
• Experience with Docker, Kubernetes, or other containerization technologies.
• Familiarity with data engineering tools and pipelines.
• Experience with monitoring tools such as CloudWatch, Prometheus, or Grafana.
• Contributions to open-source projects or notable personal AI projects.

Job Type: Full-time

Benefits:

• Health insurance
• Provident Fund

Ability to commute/relocate:

• Pimple Soudagar, Pune, Maharashtra: Reliably commute or planning to relocate before starting work (Required)

Application Question(s):

• What is your Notice period?

Experience:

• Machine learning: 1 year (Required)

Work Location: In person