AI Engineer
Direct Placement
4 days on-site, 1 day remote
5% annual bonus
120-140k base salary
The AI Engineer plays a critical role in advancing this company's mission to build more resilient,
efficient, and intelligent systems that enhance data accessibility, integrity, and scalability.
This role exists to develop and deploy AI agents, machine learning models, natural
language processing tools, and intelligent automation solutions.
By leveraging AI technologies, the AI Engineer contributes to the creation of smarter
protocols, predictive analytics, and adaptive systems that support the company's goals of
innovation and customer focus. This position is instrumental in transforming complex
data into actionable insights, enabling more intuitive user experiences, and driving
innovation across the ecosystem.
Key Responsibilities:
General Innovation ideation and POCs
Design and Develop AI models and algorithms from scratch (test, deploy, and
maintain AI systems)
Work on functional design, process design ( including scenario design, flow
mapping), prototyping, testing, training, and defining support procedures.
Articulate and document the solutions architecture and lessons learned for each
exploration and accelerated incubation.
Implement AI solutions that integrate with existing business systems to enhance
functionality and user interaction.
Manage the data flow and infrastructure for effective AI deployment
Stay current with AI trends and suggest improvements to existing systems and
workflows (conducting assessments of the AI and automation market and
competitor landscape).
Collecting and analyzing large amounts of data/programming AI software to
utilize large amounts of data
Collaborate with data scientists and other engineers to integrate AI into broader
system architectures
Advise executives and business leaders on a broad range of technology,
strategy, and policy issues associated with AI
Essential Knowledge and Experience:
Experience with innovation accelerators
Experience with cloud environments
MLOps tools: Experience using tools for lifecycle management of machine
learning models.
Docker: Experience in using Docker to create reproducible and scalable
environments.
Machine Learning Models: Advanced knowledge in Machine Learning models
and Large Language Models (LLM), using langchain or a similar tool.
LLM Implementation: Experience in implementing LLMs using vector bases and
Retrieval-Augmented Generation (RAG), as well as tuning models. Using GPTs,
Llama, or any other LLM
Solution Architecture Validation: Ability to perform solution architecture
validations for LLMs.
GenAIOps: Experience in putting Generative AI (GENAI) models into production
and providing support to them.
Knowledge of basic algorithms, object-oriented and functional design principles,
and best-practice patterns.
Experience in REST API development, NoSQL database design, and RDBMS
design and optimizations.
Education:
Bachelors Degree or Graduate Degree
3-5 years of previous relevant experience
