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Senior AI/ML Engineer
Rol remoto de AI ML Engineering con fit claro de ubicación del candidato.
Publicado4 jul 2026
Países elegibles8 países aceptados
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Resumen del rol
Senior AI/ML Engineer
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
AI/ML System Design & Leadership
- Lead the design and implementation of scalable ML systems, including supervised, unsupervised, and LLM-based solutions
- Translate research and prototypes into production-ready systems
- Partner with stakeholders to identify high-impact AI/ML opportunities and define optimal technical approaches
- Provide technical mentorship and contribute to team upskilling
LLM & Production AI Systems
- Build and operate LLM pipelines, including prompt design, fine-tuning, and evaluation
- Develop RAG-based systems using embeddings, vector stores, and retrieval strategies
- Design evaluation frameworks, feedback loops, and datasets to continuously improve model performance
- Create reusable tooling to accelerate experimentation, deployment, and monitoring
MLOps & Deployment
- Own end-to-end ML lifecycle: data pipelines, training, deployment, monitoring, and iteration
- Establish best practices for reproducibility, observability, CI/CD, and model versioning
- Partner with platform/DevOps teams to ensure reliability and scalability
- Promote responsible AI practices, including governance, fairness, and transparency
Cross-Functional Collaboration
- Lead cross-functional initiatives across data engineering, analytics, and AI/ML
- Translate complex ML concepts into clear recommendations for technical and non-technical audiences
- Collaborate with clients and internal teams to plan and deliver AI/ML solutions
- Contribute documentation, frameworks, and shared best practices
Project Execution
- Scope and lead complex AI/ML initiatives aligned to business outcomes
- Align stakeholders and drive execution across teams
- Establish clear success metrics and ensure delivery of high-impact solutions
Skills & Qualifications
- 5–7 years of experience in ML engineering, AI engineering, or related fields, with production deployment experience
- Strong programming skills in Python and SQL; experience with PyTorch and HuggingFace
- Experience building LLM applications, including RAG, embeddings, and vector search
- Experience with cloud platforms (AWS or Azure; e.g., SageMaker, Bedrock, Azure ML)
- Strong understanding of ML fundamentals: data design, training, evaluation, and experimentation
- Familiarity with LLM alignment techniques (e.g., SFT, DPO, RL)
- Experience with MLOps practices: CI/CD, monitoring, retraining, and experiment tracking
- Proficiency working with complex, multi-source datasets and defining evaluation strategies
- Strong software engineering fundamentals (testing, modularity, code review)
- Experience mentoring engineers and influencing technical direction
- Strong communication skills with both technical and non-technical stakeholders
Tech Stack
- Languages: Python, SQL
- Frameworks: PyTorch, HuggingFace
- Platforms: AWS, Azure, Snowflake, Databricks
Physical Requirements
- Frequent sitting at a desk performing work on a computer
- Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions
Core Company Values
- Take the Long View - Ensure the company is built to last
- Be Courageous - Make the right decisions even when they aren't the easiest decisions
- Be Genuine - Bring honesty and authenticity to all that you do
- Work with Focus + Passion - Display purpose and pride in your work and never stop learning
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