Role Overview

As a Mid-Level AI Engineer, you will be responsible for developing and implementing core AI features that power both our internal product framework and our client-facing solutions. You will work closely with Senior Engineers to build, test, and deploy Predictive AI models (forecasting, classification) and Agentic AI systems (autonomous workflows, tool-use LLMs). This role offers a unique opportunity to build scalable product components while gaining direct exposure to solving diverse, real-world problems for external clients.

Core Responsibilities

Feature Development & Delivery: Write clean, maintainable code to implement predictive features and Agentic workflows, ensuring they plug seamlessly into both internal products and client deployments.
Build Agentic Components: Develop autonomous agent behaviors, prompt pipelines, and multi-agent orchestration steps using modern AI frameworks.
Train Predictive Models: Pre process data, engineer features, and train/fine-tune machine learning models for forecasting, anomaly detection, and classification.
Deployment & Integration: Package models into containers (Docker) and deploy them as microservices, assisting in the setup of API endpoints for client integrations.
Testing & MLOps Support: Help track model performance, monitor agent behaviors, and run evaluation benchmarks to maintain quality and reliability across multiple environments.
Cross-Functional Collaboration: Collaborate with product teams, backend developers, and client technical points of contact to troubleshoot and optimize AI features.

Requirements

Programming: Strong proficiency in Python (writing clean, modular, and readable object-oriented code).
AI & Agentic Frameworks: Hands-on experience or solid familiarity with LangGraph, CrewAI, AutoGen, or LangChain.
Predictive ML Libraries: Practical experience with Scikit-learn, XGBoost, LightGBM, and data manipulation libraries (Pandas, NumPy).
Data & Databases: Experience working with relational databases (SQL) and basic familiarity with vector databases (e.g., Pinecone, Qdrant, Milvus).
DevOps Basics: Experience using Docker for containerization and Git for version control.

Qualifications & Experience

Experience: 2 to 4 years of professional experience in Software Engineering or Data Science, with at least 1+ years explicitly focused on building and deploying AI/ML models.
Adaptable Mindset: Eager to learn quickly and comfortably switch tasks between standard product development and fast-paced client delivery requirements.
Education: Bachelor’s degree in Computer Science, Artificial Intelligence, Data Engineering, or a related field.

· Problem Solving & Mentorship: Strong analytical problem-solving skills with a proactive mindset for troubleshooting technical challenges, alongside a readiness to guide junior peers and share knowledge within the team.

Benefits

Family Medical & Life Insurance
GYM Benefit
Schooling Allowance

Habilidades requeridas

Python (object-oriented, clean modular code)Prompt engineering and prompt pipeline designAgentic systems development and multi-agent orchestrationFamiliarity with agent frameworks (LangChain, LangGraph, AutoGen, CrewAI)Training and fine-tuning predictive ML models (forecasting, classification, anomaly detection)Feature engineering and data preprocessing using Pandas and NumPyExperience with ML libraries (Scikit-learn, XGBoost, LightGBM)Docker containerization and packaging models as microservicesBuilding and integrating API endpoints for model/service deploymentModel monitoring, evaluation, and benchmarking (MLOps support)SQL and working with relational databasesFamiliarity with vector databases (Pinecone, Qdrant, Milvus)Git version controlCross-functional collaboration and stakeholder communicationMentorship and knowledge-sharing with junior peers

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