Job Description
Develop solutions related to data architecture, Mi Platform as well as GenAl platform architect, provide tactical solution and design support to the team and embedded with engineering on the execution and implementation of processes and procedures Serve as a subject matter expert on a wide range of ML. techniques and optimirations
Provide in-depth knowledge of distributed Ml. platform deployment including training and serving Create curative solutions using GenAl workflows through advanced proficiency in large language models (LLM) and related techniques.
Gain Experience with creating a Generative Al evaluation and feedback loop for GenAl/ML, pipelines.
Get Hands on code and design to bring the experimental results into production solutions by collaborating with engineering team.
Own end to end code development in python/lava for both proof of concept/experimentation and production ready solutions
Optimize system accuracy and performance by identifying and resolving inefficiencies and bottlenecks and collaborate with product and engineering teams to deliver tailored science and technology-driven solutions.
Drives decisions that influence the product design, application functionality, and technical operations and processes
Required Qualifications, Capabilities, And Skills
4+ years of experience in building and deploying ML solutions (Deep learning/ LLMs)
In-depth understanding of classical ML. techniques, deep learning, graph networks, RAGs (and different flavours of KAG, and their applications to structured and unstructured datasets
Good understanding of frameworks such as PyTorch or TensorFlow.
In-depth understanding of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods
Hands-on experience in building and deploying either one of these two-deep leaming models or LLM based solutions
Strong mathematical foundations on how the models work
Expertise in development Deep learning/ LLMs
In-depth understanding of classical ML. techniques, deep learning, graph networks, RAGs (and different flavours of KAG, and their applications to structured and unstructured datasets
Hands-on experience in building and deploying either one of these two-deep leaming models or LLM based solutionsStrong mathematical foundations on how the models work
