GenAI Engineer

InfiCare Inc
Jersey City, United States of America
12 days ago

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Jersey City, United States of America

Tech stack

Java
Agile Methodologies
Artificial Intelligence
Amazon Web Services (AWS)
Azure
Code Review
DevOps
Python
Machine Learning
TensorFlow
Software Engineering
Reinforcement Learning
Google Cloud Platform
PyTorch
Large Language Models
Snowflake
Deep Learning
Generative AI
Scikit Learn
Kubernetes
Variational Autoencoders
Machine Learning Operations
Virtual Agents
Api Design
GPT
Docker
Databricks
Microservices

Job description

  • Design and develop scalable Gen AI and LLM/GenAI systems for complex business problems
  • Build, deploy, and operate production-grade ML and Generative AI services end-to-end
  • Build and institutionalize MLOps capabilities, including automated pipelines, monitoring, and model lifecycle management
  • Implement multi-modal RAG systems and Agentic AI architectures for enterprise solutions
  • Fine-tune and evaluate generative models (e.g., GPT-4.1) for NLP use cases, including summarization and text generation
  • Implement real-time model performance monitoring and optimization
  • Mentor and uplift junior engineers through design reviews, code reviews, and coaching
  • Communicate AI/ML capabilities and results to both technical and non-technical stakeholders

Requirements

Our client is seeking a highly skilled Senior GenAI Engineer with a strong focus on Generative AI and Agentic development. The ideal candidate will design, develop, and implement Gen AI applications and algorithms that enhance enterprise AI capabilities, while serving as a hands-on engineering leader across multiple workstreams., * Strong knowledge of NLP, LLM app development, and Agentic Design

  • Experience developing multi-modal RAG systems for enterprise solutions
  • Proficiency in Python, R, or Java
  • Experience with TensorFlow, PyTorch, Scikit-learn, and OpenAI API
  • Cloud platforms: AWS, Azure, Google Cloud Platform, Snowflake, or Databricks
  • Containerization: Docker, Kubernetes; microservices architecture
  • Understanding of statistics, deep learning, GANs, VAEs, classification, regression, time series, reinforcement learning
  • Ability to design Agentic AI architecture, including context engineering and RAG

Preferred Skills

  • Expertise in RAG pipeline design and implementation.
  • Hands-on knowledge of Chain-of-Thought, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.
  • Familiarity with the financial services industry.
  • DevOps practices and Agile methodologies.

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