GenAI Engineer || NY, NY (Onsite 3 days)

[Vinit Kumar Maurya]

Job Title - Principal Engineer (Gen AI)

Location - NY, NY (Onsite 3 days)
Employment Type - Open for both (Contract and fulltime)
Min Year of Exp - 10+ Years

Interview Process - 

• Assessment test
• Technical/ Delivery Interview
• Client Interview

Required skills

• 8+ years for Sr GenAI Engineer and 10+ years for Staff/Principal GenAI Engineer, of professional experience in building Machine Learning models & systems 
• 3+ years of hands-on experience in how LLMs work & Generative AI (LLM) techniques particularly prompt engineering, RAG, and agents.
• Expert proficiency in programming skills in Python, Langchain/Langgraph and SQL is a must.
• Understanding of Cloud services from various cloud services from Azure, GCP, or AWS for building the GenAI applications
• Excellent communication skills to effectively collaborate with business SMEs

Roles & Responsibilities

• Develop and optimize LLM-based solutions: Lead the design, training, fine-tuning, and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.
• Codebase ownership: Maintain high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.
• Cloud integration: Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.
• Cross-functional collaboration: Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.
• Mentoring and guidance: Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.
• Continuous innovation: Stay abreast of the latest advancements in LLM research and generative AI, proposing and experimenting with emerging techniques to drive ongoing improvements in model performance.