Machine Learning / Ai Engineer Lead - Databricks - Eng

Muttdata; Quilmes; Remoto; ARS 139.086.000 - 216.357.000; Tempo pieno

Vantaggi offerti da questo lavoro

USD-based pay

Remote work with flexible hours

International certifications covered

Coworking spaces worldwide

Descrizione del lavoro

Pearster is hiring a Lead AI Engineer to design and lead GenAI workflows that automate cloud security policies and remediation. You will architect agentic patterns, guide AI-powered products, and mentor engineers in a distributed team.

This fully remote role offers USD-based pay, flexible hours, and international opportunities. You will work with OpenAI, LangChain, and vector databases to deliver production-ready AI solutions for global clients.

Competenze

• Strong Python, Pandas, NumPy, and scikit-learn expertise.
• Experience with NLP libraries: spaCy, NLTK, transformers.
• Knowledge of fuzzy matching and document processing.
• ML model deployment via REST APIs, Docker, Azure ML.
• Experience with LLMs for document understanding.
• English communication and remote work ability.

Mansioni

• Develop ML models for document understanding.
• Build NLP pipelines and data processing.
• Deploy REST APIs and containerize with Docker.
• Collaborate with cross-functional teams.
• Evaluate model accuracy and scalability.

Conoscenze

Python

NLP

LLMs

Transformers

Docker

Azure ML

OpenAI

LangChain

Pandas

NumPy

Formazione

Bachelor's degree in CS or related field

Strumenti

spaCy

NLTK

Transformers

TensorFlow

PyTorch

Descrizione del lavoro

We are seeking a Machine Learning Engineer with strong expertise in Python and a focus on Natural Language Processing (NLP) to join our team. The ideal candidate will have experience in the finance or fintech domain, specifically in reconciliation and accounting.

Responsibilities:

Develop and implement machine learning models for document understanding and processing.
Utilize Python libraries such as Pandas, NumPy, and scikit-learn for data manipulation and analysis.
Apply NLP techniques using libraries like spaCy, NLTK, or transformers.
Implement fuzzy matching algorithms (., Levenshtein distance, record linkage) for data accuracy.
Process documents using OCR, PDF parsing, and Excel data extraction methods.
Deploy machine learning models as REST APIs using Docker and Azure ML.
Understand and apply supervised and unsupervised learning techniques.
Design and manage data pipelines and ETL processes.
Collaborate with cross-functional teams to meet project goals.

Required Skills :

Strong expertise in Python (Pandas, NumPy, scikit-learn).
Experience with NLP libraries (spaCy, NLTK, transformers).
Knowledge of fuzzy matching algorithms.
Document processing skills (OCR, PDF parsing, Excel extraction).
Experience in ML model deployment (REST APIs, Docker, Azure ML).
Understanding of supervised/unsupervised learning techniques.
Familiarity with deep learning frameworks (TensorFlow, PyTorch).
Experience with Large Language Models (LLMs) for document understanding (OpenAI, Azure OpenAI).
Knowledge of Azure ML services and Azure Cognitive Services.
2-5 years of experience in ML engineering, focusing on NLP or financial data processing.
Experience in the finance/fintech domain (reconciliation, accounting).

Education:

Master’s degree in Computer Science, Data Science, or a related field.

Spoiler: This is where your next big move begins.

What this Journey Looks Like

At Pearster, we are hiring an AI Engineer to join our team. This is a 100% remote position where you will be responsible for designing, building, and implementing AI-powered solutions that solve real business challenges and create meaningful impact.

You will work with modern Artificial Intelligence technologies, including Generative AI, Large Language Models (LLMs), Machine Learning, and intelligent automation, collaborating with cross-functional teams to transform ideas and business needs into scalable, production-ready solutions.

We are looking for a curious, hands-on, and innovative professional who enjoys experimenting with emerging technologies, solving complex problems, and turning the latest advances in AI into practical solutions.

What You'll Lead and Build

Build solutions leveraging Generative AI, Large Language Models (LLMs), and Machine Learning.

Design, develop, and deploy AI-powered applications and solutions.

Develop and integrate AI agents, APIs, and intelligent automation workflows.

Build solutions using RAG (Retrieval-Augmented Generation), embeddings, vector databases, and prompt engineering.

Integrate AI models and services from platforms such as OpenAI, Anthropic, Google, AWS, Azure, or similar providers.

Develop prototypes and Proofs of Concept (POCs) and help transform successful experiments into production-ready solutions.

Evaluate and optimize AI solutions for accuracy, reliability, performance, scalability, and cost.

Collaborate with Engineering, Product, and business stakeholders to identify opportunities where AI can create meaningful value.

Integrate AI capabilities into existing applications, platforms, and workflows.

Contribute to responsible AI practices, including security, privacy, monitoring, and appropriate use of AI systems.

Stay up to date with emerging AI technologies, frameworks, tools, and industry best practices.

Share knowledge and contribute to building AI capabilities and best practices across the organization.

What You Bring to the Table

Strong programming skills, preferably in Python.

Professional experience in Software Engineering, Artificial Intelligence, Machine Learning, Data Science, or a related technical field.

Hands-on experience building applications using Generative AI and Large Language Models (LLMs).

Experience integrating APIs and developing backend services.

Knowledge of prompt engineering, RAG, embeddings, vector databases, and AI agents.

Familiarity with AI frameworks and tools such as LangChain, LlamaIndex, Hugging Face, or similar technologies.

Experience working with AI APIs and platforms such as OpenAI, Anthropic, Google Gemini, or similar.

Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.

Understanding of Git, APIs, CI/CD, and modern software development practices.

Strong analytical and problem-solving skills.

Ability to work independently in a fully remote environment.

Strong communication and collaboration skills.

Intermediate to advanced English level to collaborate effectively in an international environment.

Benefits that Move you Forward.

We’re here to amplify your brilliance, not contain it.

Work from anywhere with true flexibility and freedom.

Earn in USD with compensation that matches your expertise.

Recharge confidently with dedicated paid time off.

Advance your career with fully covered international certifications.

Access coworking spaces worldwide whenever you want a professional setup.

Strengthen your English and expand your global reach.

Connect and have fun with activities that unite our international team.

Feel appreciated with personalized gifts and a thoughtful welcome kit.

Grow our community and earn through our referral program.

At Pearster, your journey matters, and we’re here to help you go further than you imagined.

LangChain.

LangGraph.

AWS.

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for a Lead AI Engineer to design and lead GenAI and agentic workflows that automate cloud security policies and remediation.

The mandatory requirements are AI architecture and strategy experience, 6+ years in software engineering on public cloud, 2+ years building agentic AI with RAG and vector databases, technical leadership, and an upper-intermediate English level.

WHAT YOU WILL DO

- AI Architecture & Strategy: architect Generative AI workflows (utilizing RAG, vector databases, and LLM APIs) to automate the creation of configuration requirements, Rego policies, and Terraform modules for new cloud services (e.g., AWS S3).

- Cloud Security Automation: oversee the development of AI-enabled drift detection, continuous discovery, and gap analysis capabilities to accelerate response and remediation activities.

- Policy & Infrastructure as Code (IaC): accelerate the generation and validation of compliant Terraform modules, Rego policies (for Wiz Custom Configuration Rules), and Regula test cases for CI/CD pipelines.

- Technical Leadership: act as the technical lead for the Cloud Security Tooling team’s AI enablement initiatives, designing service architecture, and driving extensible frameworks.

MUST HAVES

- 6+ years of software engineering experience developing applications and services hosted on public cloud infrastructure.

- 2+ years of experience with cloud infrastructure deployment pipelines and infrastructure as code (IaC).

- 2+ years of experience developing agentic models (e.g., OpenAI, Anthropic) using Retrieval-Augmented Generation (RAG) and vector databases.

- Proven capability in technical leadership in a global organization, developing technology strategy and driving technology programs.

- Proven capability to lead and mentor engineers in a distributed, asynchronous, follow-the-sun model.

NICE TO HAVES

- Hands-on experience with modern AI orchestration frameworks (LangChain, LangGraph).

- Hands-on experience writing Rego policies (Open Policy Agent) or Regula test cases.

- Hands-on experience with Terraform.

- Familiarity with cloud-native security services in Amazon Web Services, Azure, and Google Cloud Platform.

- Familiarity with PagerDuty, Atlassian Suite, and ServiceNow.

PERKS AND BENEFITS

- Professional growth : Mentorship, TechTalks, and personalized growth roadmaps.

- Competitive compensation : USD-based pay with education, fitness, and team activity budgets.

- Exciting projects : Modern solutions with Fortune 500 and top product companies.

- Flextime : Flexible schedule with remote and office options.

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

WHY JOIN US

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!

ABOUT THE ROLE

We are looking for a Senior AI Engineer to design and build agentic workflows and AI-powered products for enterprise customers in higher education.

MUST HAVES

- 5+ years of experience in a software product development environment.

- Strong proficiency with Java and Spring Boot.

- Experience with relational databases.

- Hands-on experience building AI agents, tool-using LLM applications, or orchestrated workflows.

- Experience using agents to support coding, code review, testing, release management, or incident response.

- Senior-level experience building and operating production software systems.

- Experience with TypeScript, Python, RESTful APIs, and infrastructure on AWS.

- Experience with APIs, workflow engines, event-driven architectures, integrations, or distributed systems.

- Strong understanding of testing, observability, security, permissions, and failure recovery.

- Ability to turn ambiguous opportunities into practical, reliable technical solutions.

- Strong communication skills and the ability to influence engineering practices across a team.

- Experience building enterprise SaaS or multi-tenant systems.

- Leadership mindset and ability to drive changes across different teams.

NICE TO HAVES

- Experience with multi-agent systems, RAG, vector search, agent evaluation, or human-in-the-loop workflows.

- Familiarity with higher education technology and its privacy and compliance requirements.

- Experience leading technical transformation or introducing new engineering practices.

WHAT YOU WILL DO

- Design and build agentic workflows that coordinate tools, services, data, and human approvals.

- Develop reusable patterns for agent orchestration, tool use, context management, memory, and multi-agent collaboration.

- Use agentic coding tools throughout the development lifecycle, including planning, implementation, testing, debugging, refactoring, code review, and documentation.

- Integrate agents into software development, testing, deployment, support, and operational workflows.

- Establish standards for agent evaluation, observability, reliability, security, and governance.

- Prototype new agentic capabilities and turn successful experiments into production systems.

- Build complete, usable products—not just demonstrations or isolated model integrations.

- Identify opportunities to improve engineering productivity and product delivery through agentic development.

- Mentor engineers and help shape an agentic-first technical strategy across the organization.

- Collaborate with product and engineering leaders to connect agentic capabilities to meaningful outcomes.

- Demonstrate the ability to use AI coding agents as productive engineering collaborators across an application codebase.

- Provide agents with clear context, constraints, architecture, acceptance criteria, and technical direction.

- Review, test, secure, and improve agent-generated code rather than accepting outputs without verification.

- Decompose complex application work into agent-ready tasks while maintaining architectural consistency and product intent.

- Create workflows in which agents can reason, select tools, call APIs, manage state, recover from failures, and escape to people when appropriate.

- Design agentic applications around real user needs, including intuitive experiences, reliable workflows, permissions, audit trails, and meaningful error handling.

- Move from prototype to production by addressing performance, scalability, security, monitoring, evaluation, cost, and ongoing maintenance.

- Understand problems deeply, make appropriate tradeoffs, and deliver complete solutions that customers can depend on.

PERKS AND BENEFITS

- Professional growth : Mentorship, TechTalks, and personalized growth roadmaps.

- Competitive compensation : USD-based pay with education, fitness, and team activity budgets.

- Exciting projects : Modern solutions with Fortune 500 and top product companies.

- Flextime : Flexible schedule with remote and office options.

6

Cloud AI Engineer, Google Cloud (English)

Share Cloud AI Engineer, Google Cloud (English)

_corporate_fare_ Google _place_ Buenos Aires, Argentina; Mexico City, CDMX, Mexico

**Mid**

Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.

XIn most instances, this position requires in-person interviews as part of the hiring process.

**Please submit your resume in English - we can only consider applications submitted in this language.**

Only applications of candidates with Mexican citizenship will be evaluated for this role in compliance with the provisions of Article 7 of the Federal Labor Law.

Note: By applying to this position you will have an opportunity to share your preferred working location from the following: **Buenos Aires, Argentina; Mexico City, CDMX, Mexico** .

**Minimum qualifications:**
+ Bachelor's degree in Computer Science or equivalent practical experience.
+ 3 years of experience building machine learning solutions and working with technical customers.
+ Experience designing cloud enterprise solutions and supporting customer projects to completion.
+ Experience building and deploying custom ML models into production, including experience with deep learning frameworks and with designing and deploying agentic AI workflows for business process automation.
+ Experience coding in Python, including data structures, algorithms, and software design.
+ Ability to communicate in English fluently as this is a customer-facing role.

**Preferred qualifications:**
+ Experience working with recommendation engines, data pipelines, or distributed machine learning.
+ Experience with deep learning frameworks (e.g., TensorFlow, PyTorch, XGBoost).
+ Knowledge of data warehousing concepts, including data warehouse technical architectures, infrastructure components, ETL/ ELT and reporting/analytic tools and environments (e.g., Apache Beam, Hadoop, Spark, Pig, Hive, MapReduce, Flume).
+ Understanding of the auxiliary practical concerns in production machine learning systems.

**About the job**
The Google Cloud Consulting Professional Services team guides customers through the moments that matter most in their cloud journey to help businesses thrive. We help customers transform and evolve their business through the use of Google's global network, web-scale data centers, and software infrastructure. As part of an innovative team in this rapidly growing business, you will help shape the future of businesses of all sizes and use technology to connect with customers, employees, and partners.

As a Cloud AI Engineer, you will design and implement machine learning solutions for customer use cases, leveraging core Google products including TensorFlow, DataFlow, and Vertex AI. You will work with customers to identify opportunities to apply machine learning in their business, and travel to customer sites to deploy solutions and deliver workshops to educate and empower customers. Additionally, you will work closely with Product Management and Product Engineering to build and constantly drive excellence in our products.

In this role, you will work with key Google Cloud customers. Together with the team you will support customer implementation of Google Cloud products through: architecture guidance, best practices, data migration, capacity planning, implementation, troubleshooting, monitoring, and much more.

Google Cloud accelerates every organization's ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities
+ Be a trusted technical advisor to customers and solve complex machine learning challenges.
+ Coach customers on the practical challenges in machine learning systems: feature extraction and feature definition, data validation, monitoring, and management of features and models.
+ Work with Customers, Partners, and Google Product teams to deliver tailored solutions into production.
+ Create and deliver best practice recommendations, tutorials, blog articles, and sample code.
+ Travel up to 30% in-region for meetings, technical reviews, and onsite delivery activities.
Information collected and processed as part of your Google Careers profile, and any job applications you choose to submit is subject to Google's Applicant and Candidate Privacy Policy (./privacy-policy) .

Google is proud to be an equal opportunity and affirmative action employer. We are committed to building a workforce that is representative of the users we serve, creating a culture of belonging, and providing an equal employment opportunity regardless of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), expecting or parents-to-be, criminal histories consistent with legal requirements, or any other basis protected by law. See alsoGoogle's EEO Policy ( ,Know your rights: workplace discrimination is illegal ( ,Belonging at Google ( , andHow we hire ( .

If you have a need that requires accommodation, please let us know by completing ourAccommodations for Applicants form ( .

Google is a global company and, in order to facilitate efficient collaboration and communication globally, English proficiency is a requirement for all roles unless stated otherwise in the job posting.

To all recruitment agencies: Google does not accept agency resumes. Please do not forward resumes to our jobs alias, Google employees, or any other organization location. Google is not responsible for any fees related to unsolicited resumes.

Equity is granted exclusively and discretionarily by Alphabet Inc. on the basis of an agreement concluded between you and Alphabet Inc. Alphabet Inc. is your sole contractual partner with respect to equity grants. GSU grants are not guaranteed, are discretionary, are subject to approval by the Alphabet Inc. board of directors or its delegate, the terms of the relevant Alphabet Inc. stock plan, and your grant agreement. They have no impact on statutory payments. Current or past grants do not confer an acquired right.

Google is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. See also and If you have a need that requires accommodation, please let us know by completing our Accommodations for Applicants form: