Data Scientist Manager GenAI

Montevideo - Uruguay

Monthly Salary: Not Disclosed

Posted on: 7 hours ago

Vacancies: 1 Vacancy

Job Summary

Architect implement and deploy production-ready AI solutions using LLMs transformer-based models retrieval systems and agentic workflows.
Design iterate and optimize prompts workflows and RAG pipelines to improve accuracy latency cost-efficiency and safety.
Build multi-step agentic systems capable of task decomposition tool and API invocation state management and robust reasoning chains.
Deploy and maintain GenAI pipelines across API batch and streaming production environments ensuring reliability and scalability.
Develop and maintain evaluation frameworks to measure grounding factuality latency and cost of AI systems.
Implement guardrails and safeguards including prompt-injection protection content moderation output validation loop prevention and tool-call limits.
Collaborate cross-functionally with Product Engineering and ML Ops teams to deliver high-quality AI features end-to-end.

Qualifications :

3 years of experience in applied machine learning with strong exposure to NLP transformers or generative AI systems.
Hands-on experience with LLM and agent frameworks such as LangChain LlamaIndex OpenAI API CrewAI or similar tools and services (e.g. Azure Prompt Flow AWS Bedrock agents).
Proven experience designing and operating multi-step agentic systems with appropriate safety and reliability safeguards.
Solid ML foundations with experience building and deploying models into production environments (API batch or streaming).
Strong Python skills following clean modular and production-grade coding practices.
Demonstrated ability to design run and analyze experiments using metrics-driven decision-making.
Experience deploying and monitoring AI-powered applications in cloud environments (AWS Azure or GCP) including containerization versioning and CI/CD.
Experience or strong interest in Responsible AI practices including privacy bias mitigation and safety guardrails.
Degree in Computer Science Data Science Engineering or a related field (or equivalent practical experience).
Strong collaboration skills and ability to operate at the intersection of Data Science and Engineering owning solutions from prototype to production.

What about languages

Advanced English proficiency is required for effective communication with international teams and stakeholders.

Additional Information :

Our perks and benefits:

Every day lunches! (headquarters):

Vegetarian vegan gluten and sugar free options.
Gourmet meals every Friday with our on-site chef!

Flexible working options to help you strike the right balance.

All the equipment you need to harness your talent (Macbook and accessories).

Snacks and beverages available everyday (headquarters).

After office events football tennis and game nights (headquarters).

Everyone is welcome to join our football league every Wednesdays and Fridays.

Challenge your teammates to a pool game and win the offices trophy! Tennis courts available for friendly matches.

Not a sports person Dont worry we also have chess championships game and music nights for you to join!

Learning opportunities:

AWS Certifications (we are AWS Partners).
Study plans courses and other certifications.
English Lessons.
Learn from your teammates on our Tech Tuesdays!

Mentoring and Development opportunities to shape your career path.

Anniversary and birthday gifts.

Great location and even greater teammates!

So what are the next steps

Our team is eager to learn about you! Send us your resume or LinkedIn profile below and well explore working together!

Remote Work :

Yes

Employment Type :

Full-time

Architect implement and deploy production-ready AI solutions using LLMs transformer-based models retrieval systems and agentic workflows.Design iterate and optimize prompts workflows and RAG pipelines to improve accuracy latency cost-efficiency and safety.Build multi-step agentic systems capable of ...

Architect implement and deploy production-ready AI solutions using LLMs transformer-based models retrieval systems and agentic workflows.
Design iterate and optimize prompts workflows and RAG pipelines to improve accuracy latency cost-efficiency and safety.
Build multi-step agentic systems capable of task decomposition tool and API invocation state management and robust reasoning chains.
Deploy and maintain GenAI pipelines across API batch and streaming production environments ensuring reliability and scalability.
Develop and maintain evaluation frameworks to measure grounding factuality latency and cost of AI systems.
Implement guardrails and safeguards including prompt-injection protection content moderation output validation loop prevention and tool-call limits.
Collaborate cross-functionally with Product Engineering and ML Ops teams to deliver high-quality AI features end-to-end.