You will join an extension team of our client – iBusiness Funding.
iBusiness Funding is a leader in providing innovative SaaS solutions for banks and lenders, with a specialization in SBA lending. We build scalable lending platforms that streamline the business lending process, allowing lenders to efficiently deliver capital to small and medium-sized businesses.
To date, we’ve processed over $7 billion in SBA loans and handle more than 1,000 business loan applications daily. Our team is driven by our core values of innovation, integrity, enjoyment, and family. As a top five SBA 7(a) preferred lender, our parent company offers SBA express and small loan capabilities.
Join us and be part of a team that’s transforming the finance industry and empowering businesses to thrive!
We are seeking an experienced Senior Large Language Model Engineer to join our team.
You will play a key role in designing and implementing workflows that leverage large language models (LLMs, LAMs, LMMs, LVLMs, etc.) to automate the process and drive innovation in our products. The ideal candidate will have a deep understanding of NLP, experience with foundational models, and a flexible, problem-solving mindset. You will collaborate closely with cross-functional teams, contributing to the development of scalable AI-driven solutions.
What you’ll do
Design, Implement, and optimize workflows that incorporate large language models to automate and enhance product features
Leverage existing foundational models and adapt them to fit into various product requirements, ensuring alignment with business goals
Collaborate with product managers, data scientists, and software engineers to integrate LLM-based automation into scalable solutions
Create and architect Interpreters, Agented Systems, and integrate multi-hop RAG and other LLM experiences into existing systems to coordinate knowledge responses
Research and evaluate new technologies and methodologies in the LLM space to continuously improve product automation
Work on the customization and fine-tuning of models to optimize performance for specific use cases
Develop, test, and deploy LLM-based services in production environments
Provide technical leadership and mentorship to other engineers on the team
Ensure that LLM integrations are efficient, scalable, and secure, adhering to industry best practices
What we’re looking for
Master’s or PhD in Computer Science, AI, Machine Learning, Physics or a related field
5+ years of experience in machine learning, NLP, or a related domain, with a focus on large language models
Proven experience working with foundational models in production environments
Strong programming skills in Python with experience in relevant ML libraries
Hands-on experience in deploying machine learning models at scale
Excellent communication and collaboration skills, with the ability to work cross-functionally
Problem-solving mindset, with the flexibility to adapt models and workflows to evolving product needs
Brownie points
Experience with MLOps and AWS
Familiarity with reinforcement learning and other advanced NL techniques
Experience building risk modules
Why work with us
Great work-life balance
Competitive remuneration package
Exceptional social package & special discounts
Supplemental health & dental care
Team bonding events
Excellent office location & facilities
Relaxing & gaming areas
Free bike parking & showers
More about Pwrteams
Join our fast-growing and diverse team at Pwrteams, where we provide premium IT and engineering nearshore solutions to our global customers. Since 2007, we pursue to become the market leader in assembling cross-border IT and engineering teams for customers. Our operations are strategically positioned within Eastern Europe’s dynamic tech ecosystems, from where we cater the global business landscape.
We’re at the forefront of travel, media and fintech innovation, healthcare efficiency enhancements, and others. Our goal? To connect interesting customer projects and skilled talent alike. Become a part of our team and take the next step on your personal career journey.
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