At a glanceSummarised by Seekless from the posting.
Must have7
Strong experience with modern AI techniques — LLMs, prompting, fine-tuning, RAG, vector databases
Strong Python engineering background, with clean, modular coding practices
Comfortable building systems in a solo or small-team setting with limited structure
Familiarity with cloud deployment (AWS/GCP), Docker, and production ML pipelines
Able to balance product thinking and engineering discipline
Startup mentality: comfortable with ambiguity, fast iteration, and wearing many hats
Excellent communication skills — able to work directly with clients and non-technical stakeholders
Nice to have4
Experience in Conversational AI and Voice AI systems
Knowledge of ML frameworks (PyTorch, scikit-learn, etc.)
Exposure to frontend (JS/React) or dev tooling is a plus
MLOps / CI/CD familiarity
Skills
Python
AWS
GCP
Docker
LLMs
RAG
vector databases
PyTorch
scikit-learn
JS
React
About Scalepex
Scalepex is a nearshore partner that empowers businesses to scale efficiently and effectively through exceptional services. With a strong focus on customer success, innovation, and cultural alignment, we support companies in building high-performing teams across industries.
At Scalepex, we believe in fostering an environment where our people can thrive, grow their careers, and make a real impact.
Role Overview
What You’ll Do
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Start by building foundational AI capabilities and products, with opportunities to grow into leading teams and larger initiatives as they scale
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Lead the design and development of end-to-end AI applications — from prototype to production
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Work closely with founders, clients, and other engineers to shape both technical and product direction
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Build and deploy LLM-based systems, including RAG pipelines, fine-tuned models, and embedding search
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Own delivery of real-world solutions: architect, implement, test, ship
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Iterate rapidly: explore new tools, try new ideas, and solve problems creatively
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Operate with autonomy in a fast-changing environment — sometimes as the only engineer on a project
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Occasionally dive into adjacent areas like frontend, dev tooling, or data pipelines as needed
Requirements
Must-Have Skills
•
Strong experience with modern AI techniques — LLMs, prompting, fine-tuning, RAG, vector databases
•
Strong Python engineering background, with clean, modular coding practices
•
Comfortable building systems in a solo or small-team setting with limited structure
•
Familiarity with cloud deployment (AWS/GCP), Docker, and production ML pipelines
•
Able to balance product thinking and engineering discipline
•
Startup mentality: comfortable with ambiguity, fast iteration, and wearing many hats
•
Excellent communication skills — able to work directly with clients and non-technical stakeholders
Bonus Skills
Nice to have
•
Experience in Conversational AI and Voice AI systems
•
Knowledge of ML frameworks (PyTorch, scikit-learn, etc.)
•
Exposure to frontend (JS/React) or dev tooling is a plus