Python AWS Glue

Who are we looking for?

A highly skilled individual who can work efficiently using programming language Python, AWS Glue and Hands-on experience including databases (SQL/NoSQL/Graph), web frameworks, APIs with 6 + years of relevant experience.

Technical Skills:

Python, AWS Glue, Hands-on experience in applied AI/ML engineering, backend development - databases (SQL/NoSQL/Graph), web frameworks, APIs, and microservices.

Process Skills:

Core Skills :

6+ years of engineering experience with strong programming skills in Python, AWS Glue with experience in developing and maintaining production-level code.
Good to have hands-on experience in applied AI/ML engineering with 2+ years, with a track record of developing and deploying business-critical machine learning models and applications in production.s
Proficient in programming languages like Python for model development and experimentation, and integration with GenAI platform.
Strong collaboration and communication skills to work effectively with geographically spread cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
Strong problem-solving and analytical skills with emphasize on attention to detail.
Experience with cloud platforms, for deploying and scaling AI/ML models.

Desired skills but not mandatory:

Experience in backend development, including databases (SQL/NoSQL/Graph), programming languages (Python/Java/Node.js), web frameworks, APIs, and microservices and possess front-end development skills.
Knowledge of SRE practices.
Knowledge of large language models (LLMs) and accompanying toolsets the LLM ecosystem (e.g. Lang chain, Vector databases, opensource Models like Mistral, Llama, etc)
Exposure to cloud automation technologies such as Terraform
Assess and choose suitable LLM tools and models for diverse tasks including but not limited to curating custom datasets and fine-tune LLM with a focus on parameter-efficient, mixture-of-expert, and instruction methods designing and developing advanced LLM prompts, Retrieval-Augmented Generation (RAG) solutions, and Intelligent agents for the LLMs and executing experiments to push the capability limits of LLM models and enhance their dependability.