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 adecuado 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-

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