Technical Architect

About the job

Job Title – Technical Architect

Duration – FTE

Location: Edison, NJ

Job Description:

• At Least 1 year of experience in Generative AI and LLM models like GPT4, GPT3.5, Palm family of Models, Llama2, Falcon, Anthropic .

• Must know how to do the instruction tuning of LLM like llam2 etc.

• Must Know Python , should be an expert of Python.

• Must have prior experience in Pre-trained models (small and large both) , should know the applications of transfer learning.

• Must have proven experience and expertise in Data analysis, pre-processing, EDA and Data Visualization .

• At least 5 years of extensive experience in Machine Learning algorithms development, Deep Learning ( should be an expert in Neural networks including but not limited to , ANN, CNN, RNN ( LSTM ( BiLSTM, GRU both) ).

• Must have implemented at least 5 Deep Learning applications at large scale, with terabytes of training data.

• Must have proven expertise in Hyperparameter tuning, deployment and monitoring of Deep Learning Applications.

• Must know frameworks like tensorflow, Pytorch etc.

• Must know and hands on experience in one of the cloud platforms; Azure/GCP/AWS

• Prior experience in Manufacturing domain is desirable and preferred.

Qualifications:

Technical Qualifications

Hands-on programming skill on at least one language node.js/Java/Python

Strong hands-on capabilities on “Artificial Intelligence” and “Machine Learning” PaaS components such as:

• Contextual Conversation design– for personalized and humanized interaction with end user for complex business cases

• 7-10 years of experience

• Microsoft BOT service, Google DialogFlow EX, Amazon Lex

• NLP model - design, training and publishing for multiple languages

• Project experience and/or skills Certification with generative AI including: Azure Open AI (GPT 3.5/4) , Google PaLM 2 and AWS Bedrock

• Custom Speech model - Speech-to-text and Voice synthesis calibrated for language, accent, pitch, tone, noise and business vocabs.

Standard architectural practices as below:

• Omni-Channel Integration for AI. MLOPS knowledge.

• Deployment and publish for AI and ML services with ACR, ACI, Docker, Azure Kubernetes

• Azure/ AWS/ GCP certifications, AWS Machine Learning Specialty, Google Certified Cloud Engineer and Deeplearning.ai certifications on LLMs, prompt engineering

• Web app and services – Micro services, Azure functions, Logic apps, API management