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Required ML Data Engineer with Vector DB and Gen AI Skills - Remote at Remote, Remote, USA
Email: [email protected]
Hello Sree,

Momento USA is a global technology consulting, talent
acquisition and creative development firm that addresses clients' most pressing
needs and challenges.

We are currently looking for ML Data Engineer with Vector DB
and GenAI Skills Remote !!!. Please see the job description below for
your reference

Key Skills : VectorDB, Databricks

AI Developer III

  Job Description: ML Data Engineer with
Vector DB and GenAI Skills.

The candidate needs to be situated in either the EST or
CST time zone and should be willing to commute to the office in PA upon client
request. The client will cover the associated expenses.

Location: Remote

Reports To: AI Competency Lead

Summary:

We are seeking a passionate and skilled ML Data Engineer
(Band 4B) to join our team in the USA. You will play a pivotal role in building and
maintaining the data infrastructure and pipelines for our cutting-edge
Generative AI applications. You will collaborate closely with the Generative AI
Full Stack Architect and MLOps Engineer to ensure the quality, security, and
accessibility of data for our Generative AI models.

Responsibilities:

Design, develop, and implement data pipelines for
ingesting, pre-processing, and transforming unstructured data (Image, .pdf,
Audio, video) for Generative AI model training and inference.

Need to have some level of understanding or working
experience with Vector DBs ( Like Pinecone , Redis , Chroma) Understanding on
Large Language Models ( Llama , GPT-4 , Claude 2.0 ) to do text summarization
, entity extraction and classification.

Build and maintain efficient data storage solutions,
including data lakes, warehouses, and databases, appropriate for large-scale
generative AI datasets.

Implement data security and governance policies to ensure
the privacy and integrity of sensitive data used in Generative AI projects.

Collaborate with data scientists and engineers to
understand data requirements for Generative AI models and translate them into
efficient data pipelines.

Monitor and optimize data pipelines for performance,
scalability, and cost-effectiveness.

Stay up to date on the latest advancements in data
engineering tools and technologies (e.g., Apache Spark, Airflow, Snowflake,
Data Bricks ) and apply them to our Generative AI platform.

Document data pipelines and processes for clarity and
transparency.

Communicate effectively with technical and non-technical
stakeholders about data quality and availability for Generative AI projects.

Qualifications:

Bachelors degree in computer science, Data Science,
Statistics, or a related field, or equivalent experience.

6+ years of experience in data engineering or related
roles, such as data pipeline development, data storage, or ETL/ELT processes.

Proven experience building and maintaining data pipelines
for machine learning projects.

Strong understanding of data modeling principles, data
quality measures, and data security best practices.

Proficient in programming languages like Python, SQL, and
scripting languages (e.g., Bash, Shell).

Familiarity with cloud platforms (e.g., AWS, GCP, Azure)
for data storage and processing along with GenAI services like (AWS BedRock)
Excellent communication, collaboration, and problem-solving skills.

Ability to work independently and as part of a team.

Passion for Generative AI and its potential to solve
real-world challenges.

Must have

Senior individual contributor with substantial data
engineering expertise and leadership experience.

Manages complex data projects and initiatives with
independent decision-making authority.

Provides technical guidance and mentorship to junior team
members.

Has a demonstrated track record of success in delivering
impactful data solutions

Thanks,

Adil M

Sr. Technical Lead

Momento
USA
| Exceeding Customer Expectations

Email: 
[email protected]

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Keywords: artificial intelligence machine learning database information technology Pennsylvania
[email protected]
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Thu Feb 29 00:24:00 UTC 2024

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