Machine Learning Engineer (exp with ML, NLP and DL)- with a leading software development company- Pune/ REMOTE **Immediate Joiners** Full Time

Position: Machine Learning Engineers

Experience: 3 Yrs

Work Mode: Remote

No of Position: 2

Desired Candidate Profile:

 Minimum 5-year experience in NLP, Minimum 2 years of experience in Gen AI

 Master’s degree in computer science, Computer Engineering, relevant technical field, or

equivalent practical experience.

 Programming experience in Python.

 Hands-on experience ML, NLP and DL. Research experience in them is a plus.

 Hands-on experience with DL frameworks like Py Torch/Tensorflow. Must have experience with

training/finetuning and deploying a DL NLP model.

 Hands-on experience with instruction tuning and SFT on LLM. Experience with model

quantization or computational optimizations is a plus.

 Proficient in leveraging Lang Chain for the development and integration of LLMs into diverse

applications, demonstrating a strong ability to enhance AI functionalities through

innovative uses of natural language processing and understanding.

 Experience with developing ML models at scale from inception to production.

 Familiarity with one of the cloud platforms like AWS, GCP, and Azure.

 Experience with applying LLMs in the Healthcare space is a plus.

 Must obtain work authorization in the country of employment at the time of hire and maintain

ongoing work authorization during employment.

Job Responsibilities:

 Design methods, tools, and infrastructure to push forward the state of the art in large language

models.

 Deploy, monitor, and manage ML models in production, ensuring high availability and low

latency.

 Manage and optimize ML infrastructure, including cloud services, containers, and orchestration

tools.

 Define research goals informed by practical engineering concerns.

 Contribute to experiments, including designing experimental details, writing reusable code,

running evaluations and organizing results.

 Adapt standard machine learning methods to best exploit modern parallel environments (e.g.

distributed clusters, multicore SMP, and GPU)