Level AI - Software Engineer - NLP Pipeline (2-3 yrs)

Competencies :

- Python, SQL, data structures, data engineering, data querying and manipulation, algorithms, coding, problem solving, MLOps, monitoring, communication.

Roles and Responsibilities :

Big picture :

- Innovate, build, and invent on the behalf of our customers.

- Plan, execute and maintain entire products/features which millions of customers will experience.

- Collaborate with cross-functional teams to integrate/upgrade AI applications into company's products and services.

- Contribute in achieving high engineering standards across different services

- Optimize the performance, scalability and efficiency of existing and new AI applications and services

- Build, deploy and own scalable production NLP pipelines from data processing to model deployments

- Build post-deployment monitoring and continual learning capabilities.

- Propose suitable evaluation metrics and establish benchmarks

- Suggest impactful improvements and own the post-deployment MLOps service activities completely

- Setup internal pipelines to automate workflows to increase efficiency and productivity of internal teams

- Contribute in prompt engineering efforts within the team

- Active participation in bugs identification, resolution, RCA, testing, etc

We'll love to explore more about you if you have :

Qualification :

- B.E/B.Tech/M.E/M.Tech from tier 1/2 Engineering institutes with relevant work experience with a top technology company with 2-3 years of experience in Software Engineering

- Proficient in Python and SQL

- Strong Experience with API integrations and automating workflows

- Knowledge of basic Data Structures and Algorithms, and proficient in coding

- Knowledge and experience with data engineering, data mining, feature extraction pipelines, etc

- Knowledge and experience with dashboards, monitoring and logging tools like datadog, grafana, ELK stack, etc

- Basic understanding of NLP problems such as text classification, entity tagging, information retrieval, question-answering, natural language generation, clustering, etc

- Basic experience with common machine learning libraries like numpy, pandas, nltk, etc

- Experience with prompt engineering and automating/streamlining related processes

- Experience in following clean, elegant, modular, bug-free coding practices in languages like Python

- Experience with ML model deployments using REST API, Docker, Jenkins, Helm, Kubernetes, etc

- Experience with monitoring, logging and debugging tools using ELK stack, grafana, datadog, prometheus, etc

- Knowledge of cloud platforms (AWS/Azure/GCP) and their machine learning services is desirable

- Knowledge of real-time streaming tools/architectures like Kafka, Pub/Sub is desirable