Remote fresher entry level Machine Learning Enginner 1 hiring at Swiggy

Company Description

Swiggy stands as India’s largest on-demand delivery platform, driven by cutting-edge technology and a customer-first philosophy.
Launched in 2014 as a hyperlocal food delivery service, Swiggy has evolved into a logistics powerhouse.
The company serves over 500 cities in India, connecting customers with a vast array of restaurants.
With an impressive fleet exceeding 2 lakh independent delivery executives, Swiggy ensures timely and reliable service.
Boasting an employee base of over 5,000, Swiggy prioritizes a high-quality delivery experience for customers and a supportive environment for employees.
Swiggy’s rapid growth is fueled by advanced machine learning (ML) and vast data processing, enabling quick, seamless service.
Swiggy recently introduced new verticals, including Swiggy Instamart, Swiggy Genie, and Guiltfree, diversifying its services and expanding opportunities for employees.

Swiggy seeks a proficient Machine Learning Engineer with a strong grasp of ML fundamentals and software development principles.
This role emphasizes expertise in Spark, SQL, Python, and experience with large-scale data processing.
Candidates should be familiar with cloud technologies, including AWS, Azure, or GCP, and possess skills in advanced machine learning tools.
Responsibilities include building and deploying ML models across various platforms, collaborating with the data science team, and optimizing ML workflows.
This role is integral to enabling data-driven decision-making by building tools and systems that streamline ML operations.
Candidates will have the chance to contribute to Swiggy’s data product offerings and help integrate sophisticated algorithms.
This position offers opportunities to work on innovative projects that directly impact millions of users and drive Swiggy’s logistical excellence.

Technical Skills:
- Programming Languages: Proficiency in Python and SQL for data manipulation and analysis.
- Data Processing Frameworks: Experience with Apache Spark for large-scale data processing.
- API Integration: Ability to work with API integration to connect various systems and services.
- Cloud Technology: Knowledge of cloud stacks, including AWS, Azure, or GCP.
- Machine Learning Tools: Familiarity with TensorFlow, PyTorch, ONNX, and frameworks like Langchain (advantageous but not mandatory).
- Orchestration Tools: Experience with Kubernetes for container orchestration and efficient model deployment.
Core Competencies:
- ML Fundamentals: Strong understanding of machine learning concepts, algorithms, and OOPs.
- ETL Processes: Capable of developing and managing ETL pipelines to handle data at scale.
- Business-Focused Thinking: Ability to think backward from business needs to create relevant data solutions.
- Collaborative Approach: Able to work seamlessly with cross-functional teams, especially with the data science team, to meet organizational goals.
Educational Background:
- Degree in Computer Science, Data Science, Machine Learning, or a related field (preferred but not required if experience compensates).

Core Responsibilities:
- Model Deployment: Develop and deploy machine learning models across different platforms, utilizing both in-house and external cloud services.
- Data-Driven Decision Making: Work closely with data scientists to ensure ML solutions align with performance requirements, including considerations for scalability, latency, and throughput.
- Algorithm Adoption: Drive the integration of complex algorithms and data mining methods to enhance Swiggy’s data-driven capabilities.
- Product Development: Actively contribute to Swiggy’s suite of data products, supporting the enhancement of current services and the introduction of new, data-powered offerings.
Equal Opportunity Statement:
- Swiggy is committed to fostering an inclusive and equitable workplace.
- All applicants are considered for employment without discrimination based on race, color, religion, sex, disability status, or other legally protected characteristics.