AI Summary
AI/ML Engineer role involves bringing machine learning capabilities into production, working across the full arc of ML initiatives, and shaping problem definitions through to deployed, maintainable systems.
Key Highlights
Hands-on engineering role
Work across the full arc of ML initiatives
Strong software engineering foundation and AI/ML experience
Key Responsibilities
Work across the full arc of ML initiatives
Shaping problem definitions through to deployed, maintainable systems
Strong communication skills to explain model behavior and tradeoffs clearly
Technical Skills Required
Benefits & Perks
Long-term contract opportunity
Fully remote work
Strong English communication skills required
Nice to Have
Experience with LLMs
Familiarity with MLOps tooling
Background in financial services, healthcare, or another regulated industry
Job Description
Our client is a technology-forward company seeking an AI/ML Engineer to help bring machine learning capabilities into production. This is a hands-on engineering role — you'll work across the full arc of ML initiatives, from shaping problem definitions through to deployed, maintainable systems. You have a strong software engineering foundation and enough AI/ML experience to work independently, with room to deepen your expertise on the job.
This is a long-term contract opportunity without an end date. The engagement is fully remote. Candidates must be located in Latin America. You'll need strong English communication skills (B2+ or higher) for this role.
Mandatory Requirements
- 5+ years of software engineering experience, with at least 1-2 years working on AI/ML projects in a professional setting.
- Strong Python proficiency and comfort with the core ML/data ecosystem (e.g., scikit-learn, pandas, NumPy).
- Hands-on experience with at least one major ML framework (PyTorch, TensorFlow, or similar).
- Practical experience taking ML models from development into production, including deployment and monitoring.
- Working knowledge of cloud infrastructure (AWS, GCP, or Azure) for ML workloads.
- Strong communication skills — you can explain model behavior and tradeoffs clearly to both technical and non-technical stakeholders.
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- Experience with LLMs — fine-tuning, RAG architectures, or building LLM-powered applications.
- Familiarity with MLOps tooling (e.g., MLflow, Weights & Biases, Airflow).
- Background in financial services, healthcare, or another regulated industry.
- Contributions to open-source ML projects or public technical work.
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- Completed background checks will be required before the start date if you are selected as a winning candidate.
- As a winning candidate, you will be required to disclose your engagement with DevEngine as a primary client on your professional LinkedIn profile.
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