Data Research Engineer-AI/ML

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The Data Extraction Team is a brand-new team who plays a crucial role in our organization by designing, implementing, and overseeing advanced web scraping frameworks. Their core function involves creating and refining tools and methodologies to efficiently gather precise and meaningful data from a diverse range of digital platforms. Additionally, this team is tasked with constructing robust data pipelines and implementing Extract, Transform, Load (ETL) processes. These processes are essential for seamlessly transferring the harvested data into our data storage systems, ensuring its ready availability for analysis and utilization.

A typical day in the life of a Data Research Engineer will involve coming up with ideas regarding how the company/team can best harness the power of AI/LLM, and use it not only simplify operations within the team, but also to streamline the work of the research team in gathering/retrieving large sets of data. The role is that of a leader who sets a vision for the future of AI/LLMs use within the team and the company. They think outside the box and are proactive in engaging with new technologies and developing new ideas for the team to move forward in the AI/LLM field. The candidate should also at least be willing to acquire some basic skills in scraping and data pipelining.

Responsibilities:

Develop methods to leverage the potential of LLM and AI within the team.

Proactive at finding new solutions to engage the team with AI/LLM, and streamline processes in the team.

Be a visionary with AI/LLM tools and predict how the use of future technologies could be harnessed early on so that when these technologies come out, the team is ahead of the game regarding how it could be used.

Assist in acquiring and integrating data from various sources, including web crawling and API integration.

Stay updated with emerging technologies and industry trends.

Explore third-party technologies as alternatives to legacy approaches for efficient data pipelines.

Contribute to cross-functional teams in understanding data requirements.

Assume accountability for achieving development milestones.

Prioritize tasks to ensure timely delivery, in a fast-paced environment with rapidly changing priorities.

Collaborate with and assist fellow members of the Data Research Engineering Team as required.

Leverage online resources effectively like StackOverflow, ChatGPT, Bard, etc., while considering their capabilities and limitations.

Skills and Experience

Bachelor's degree in Computer Science, Data Science, or a related field. Higher qualifications is a plus.

Think proactively and creatively regarding the next AI/LLM technologies and how to use them to the teams and companys benefits.

Think outside the box mentality.

Experience prompting LLMs in a streamlined way, taking into account how the LLM can potentially hallucinate and return wrong information.

Experience building agentic AI platforms with modular capabilities and autonomous task execution. (crewai, lagchain, etc.)

Proficient in implementing Retrieval-Augmented Generation (RAG) pipelines for dynamic knowledge integration. (chromadb, pinecone, etc)

Experience managing a team of AI/LLM experts is a plus: this includes setting up goals and objectives for the team and fine-tuning complex models.

Strong proficiency in Python programming

Proficiency in SQL and data querying is a plus.

Familiarity with web crawling techniques and API integration is a plus but not a must.

Experience in AI/ML engineering and data extraction

Experience with LLMs, NLP frameworks (spaCy, NLTK, Hugging Face, etc.)

Strong understanding of machine learning frameworks (TensorFlow, PyTorch)

Design and build AI models using LLMs

Integrate LLM solutions with existing systems via APIs

Collaborate with the team to implement and optimize AI solutions

Monitor and improve model performance and accuracy

Familiarity with Agile development methodologies is a plus.

Strong problem-solving and analytical skills with attention to detail.

Creative and critical thinking.

Ability to work collaboratively in a team environment.

Good and effective communication skills.

Experience with version control systems, such as Git, for collaborative development.

Ability to thrive in a fast-paced environment with rapidly changing priorities.

Comfortable with autonomy and ability to work independently.