Responsibilities of an engineer on our team

Depending on your seniority and background, your responsibilities will typically span the full lifecycle of software, data, and machine learning projects. You may focus primarily on backend and data engineering, ML engineering, or a combination of both. If your expertise is mainly in backend and data engineering and you have little or no ML experience, that's completely fine.

Typical responsibilities include:

- Collecting data from disparate sources
- Analyzing collected data
- Visualizing and communicating insights to stakeholders
- Designing and training machine learning models
- Designing and building APIs, services, and pipelines for data-intensive applications
- Architecting and implementing MLOps systems so models can be deployed and operated in production
- Rapidly prototyping concepts to validate different aspects of data and ML pipelines
- Mentoring junior engineers
- Participating in pre-sales and sales conversations, if interested, to gain exposure to how products are taken to market
- Documenting and publishing your work as technical blog posts

Tools and technologies we routinely use:

- Python and shell scripting for day-to-day programming
- Amazon S3, Athena, Glue, and Lake Formation for data management and ETL
- Amazon EC2, ECS, Docker, Terraform, and CloudFormation for infrastructure and deployments
- Serverless backend applications using AWS Lambda, Step Functions, and API Gateway
- Batch processing pipelines and scheduled workflows
- Event-driven and asynchronous backend systems
- Postman, FastAPI, and REST APIs for developing, testing, and integrating backend services




- Redis and in-memory caches for performance optimization
- Logging, monitoring, and observability tooling for production systems
- Test-driven development (TDD), unit testing, and integration testing
- BeautifulSoup, Selenium, and OpenCV for text, web, and visual data extraction
- OpenAI, Gemini, and Claude APIs for generative AI applications
- Fine-tuning small and large language models
- Training custom machine learning models using PyTorch
- Git, GitHub, GitHub Actions, and CircleCI for version control and CI/CD
- Excel and Google Sheets for quick analysis and lightweight reporting
- SQL (PostgreSQL, MySQL) and NoSQL (MongoDB) databases
- Pandas, NumPy, and Scikit-learn for data analysis and classical machine learning
- Streamlit and React for rapid application prototyping

Depending on the project, you may spend most of your time on a particular subset of these responsibilities. We encourage engineers to be patient, stay curious, and remain open to working across the entire lifecycle of a project. We value engineers who are interested in understanding and contributing to the whole pipeline, not just a single part of it.

Qualifications

- Preferably one solid project which you have driven end-to-end. For backend engineers and data engineers: evidence of setting up data piplines on cloud-based infrastructure and integrating with various apps; and ML engineers: any work utilizing either NLP or computer vision stacks.
- Preferable if you can share a portfolio of work that you can show off - either on Github, Kaggle, your personal webpage, or something similar.

📌 Senior Full Stack Engineer (New Delhi)
🏢 Unsaidtalks Education
📍 New Delhi