ML Engineer

Armeta is developing advanced AI-driven systems that transform how large-scale engineering and construction projects are evaluated and approved. Our technology automates complex, compliance-heavy processes, ensuring accuracy, scalability, and trustworthiness across critical infrastructure initiatives.
We work at the intersection of AI, data, and engineering intelligence, building tools that support decision-making in domains where reliability and compliance are paramount. And we are looking for a Machine Learning Engineer

Key Responsibilities:

• Data Analysis: Conduct data preprocessing, exploratory data analysis, feature engineering, and model validation.

• End-to-End Delivery: Take ownership of the full machine learning lifecycle, including training, testing, and deploying models into a production environment.

• Engineering & Integration: Write clean, scalable backend code (Python) to wrap your ML services and integrate them with new and existing systems.

• Data Strategy: Collaborate with the Research/Analytics team to guide labeling efforts and build robust datasets for future training.

• Innovation: Explore new business cases and identify areas where ML solutions can drive value.

• Collaboration: Work closely with stakeholders and cross-functional team members to align technical output with business goals.

• Multiple modalities: Build systems across different modalities, like Vision and Text, including VLMs and specialized models.

Your First 90 Days:

• Analyze & Evaluate: Review the current State of the Art in relevant fields (CV, NLP) and perform evaluations on custom data to find the best candidates for deployment.

• Data Foundation: Analyze current data availability and build up datasets to support immediate and future models.

• Ship Code: Train, test, and deploy open-source machine learning models to get your first services live.

Qualifications:

• Experience: 2+ years of professional experience in Machine Learning, Data Science or a related technical field.

• Core Fundamentals: A solid grasp of Machine Learning fundamentals, Statistics, and Probability.

• Software Engineering Excellence: Proficiency in Python. You must be able to write clean, readable, and scalable code. You will also need to know about caching strategies, message queues, rate limiting, monitoring, and batching.

• Adaptability: Ability to navigate between research (model tuning) and engineering (system integration).

• Agents and Agentic workflows: Experience with building Agentic systems using Langgraph, Langchain and other frameworks with tool use for long-running tasks.