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

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