Job Description: AI/ML Python Developer with AWS
( Interns/ Freshers DON'T APPLY)
Position: AI/ML Python Developer
Department: Technology / Artificial Intelligence
Employment Type: Full-Time on site Mohali.
Experience: 2-5 Years
Location: Mohali, Punjab
About the Role
We are looking for an AI/ML Python Developer with strong Python programming skills, practical knowledge of Machine Learning and Artificial Intelligence, and good hands-on understanding of AWS Cloud Services.
The candidate will be responsible for developing AI/ML solutions, building scalable Python-based backend systems, deploying models and applications on AWS, creating APIs, working with databases and data pipelines, and supporting production-grade cloud infrastructure.
We are looking for someone who can work beyond notebooks and experiments and can take a solution from:
Data → Model → API → Cloud Deployment → Monitoring → Production
Key Responsibilities
- Develop and maintain AI/ML applications using Python.
- Build, train, evaluate, and optimize Machine Learning models.
- Work with supervised and unsupervised learning techniques.
- Perform data preprocessing, feature engineering, data cleaning, and model validation.
- Develop scalable backend services and REST APIs using FastAPI, Flask, or Django.
- Integrate AI/ML models with web and mobile applications.
- Deploy Python applications and ML models on AWS.
- Build and manage cloud-based AI/ML infrastructure.
- Develop data processing and automation pipelines.
- Work with structured and unstructured datasets.
- Optimize application performance and model inference.
- Build batch-processing and real-time processing systems.
- Implement logging, monitoring, error handling, and alerting.
- Work with Git-based development workflows and CI/CD pipelines.
- Write clean, modular, reusable, tested, and well-documented Python code.
- Collaborate with frontend, backend, DevOps, data, and product teams.
- Troubleshoot production issues related to applications, models, databases, APIs, and cloud infrastructure.
- Follow security and scalability best practices while developing production systems.
Python Skills Required
The candidate should have strong knowledge of:
- Python
- Object-Oriented Programming
- Data Structures and Algorithms
- Async programming
- Multithreading / multiprocessing fundamentals
- Exception handling
- File handling
- API development
- Package management
- Virtual environments
- Testing and debugging
- Performance optimization
Hands-on experience with libraries/frameworks such as:
- NumPy
- Pandas
- SciPy
- Scikit-learn
- Matplotlib
- PyTorch and/or TensorFlow
- FastAPI / Flask / Django
- Pydantic
- SQLAlchemy
AI/ML Knowledge
The candidate should understand:
- Regression
- Classification
- Clustering
- Decision Trees
- Random Forest
- Gradient Boosting
- XGBoost / LightGBM
- Support Vector Machines
- KNN
- Feature Engineering
- Feature Selection
- Cross Validation
- Hyperparameter Optimization
- Bias and Variance
- Overfitting and Underfitting
- Model Evaluation Metrics
- Time-Series fundamentals
- Neural Networks
- Deep Learning fundamentals
- Model Training and Inference
- Model Serialization
- Model Deployment
- Model Monitoring
Knowledge of NLP, Computer Vision, LLMs, Transformers, RAG, or Generative AI will be an added advantage.
AWS Cloud Knowledge
The candidate should have good practical knowledge of AWS services, particularly:
- EC2 – application and model hosting
- S3 – dataset, file, model, and object storage
- Lambda – serverless processing
- API Gateway – API management
- RDS – managed relational databases
- DynamoDB – NoSQL databases
- ECR – container image repository
- ECS – containerized application deployment
- CloudWatch – logs, monitoring, metrics, and alerts
- IAM – roles, users, permissions, and security
- VPC – networking fundamentals
- SQS – asynchronous job queues
- SNS – notifications
- Secrets Manager / Parameter Store – secure credential management
Knowledge of the following will be an advantage:
- AWS SageMaker
- AWS Bedrock
- Step Functions
- EventBridge
- Glue
- Athena
- Redshift
- Kinesis
- Elastic Load Balancing
- Auto Scaling
- Route 53
- CloudFront
Database Knowledge
Candidate should be comfortable working with:
- PostgreSQL
- MySQL
- MongoDB and/or DynamoDB
- Redis
Should understand:
- Database schema design
- SQL queries
- Indexing
- Joins
- Query optimization
- Transactions
- Caching
- Large dataset handling
DevOps & Deployment
Good understanding of:
- Git
- GitHub / GitLab
- Docker
- Linux
- CI/CD
- Environment management
- Application deployment
- Logging and monitoring
- Production debugging
Knowledge of Kubernetes and Infrastructure as Code tools such as Terraform or AWS CloudFormation will be an advantage.
API & Backend Development
Candidate should be capable of developing production-grade APIs including:
- REST APIs
- Authentication and authorization
- JWT
- API validation
- Error handling
- Background jobs
- WebSockets
- Rate limiting
- API documentation
- Third-party API integrations
Experience with FastAPI will be highly preferred.
MLOps Knowledge
Understanding of the complete ML lifecycle is preferred:
Data Collection → Data Processing → Feature Engineering → Training → Evaluation → Model Registry → Deployment → Monitoring → Retraining
Experience with tools such as MLflow, DVC, SageMaker, Docker, CI/CD or similar MLOps technologies will be an added advantage.
Required Qualifications
- Bachelor's degree in Computer Science, IT, AI/ML, Data Science, Engineering, or a related discipline.
- Strong Python programming skills.
- Practical understanding of Machine Learning.
- Hands-on experience with Python ML/data libraries.
- Good understanding of AWS Cloud services.
- Experience developing APIs and backend applications.
- Understanding of databases and SQL.
- Knowledge of Git and software development practices.
- Ability to independently debug technical problems.
- Strong analytical and problem-solving skills.
Preferred Qualifications
Candidates with experience in any of the following will be preferred:
- Production ML deployment
- AWS SageMaker
- Generative AI / LLM applications
- RAG systems
- Vector databases
- NLP
- Computer Vision
- Recommendation systems
- Time-series modelling
- Real-time data processing
- Financial or quantitative applications
- High-performance Python systems
- Microservices architecture
- Docker / Kubernetes
- Cloud architecture
Ideal Candidate
The ideal candidate should not be limited to creating ML models inside Jupyter notebooks.
They should be capable of building an end-to-end production system such as:
Dataset → Python Processing → ML Model → FastAPI → Docker → AWS → Database → Monitoring → Production Application
The candidate should have a strong engineering mindset and should be comfortable converting AI/ML research and ideas into reliable, scalable, production-ready software.
Key Skills
Python | Artificial Intelligence | Machine Learning | Deep Learning | Scikit-learn | PyTorch | TensorFlow | Pandas | NumPy | FastAPI | REST API | AWS | EC2 | S3 | Lambda | RDS | DynamoDB | SageMaker | Docker | Git | PostgreSQL | Redis | CI/CD | MLOps | Linux | CloudWatch
Pay: ₹35,000.00 - ₹50,000.00 per month
Work Location: In person
