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Artificial intelligence model developer

Itu
beBeeMachineLearning
Modelista
Anunciada dia 17 novembro
Descrição

About the Role


We are seeking a talented Machine Learning Engineer to join our growing AI team.



Main Responsibilities:



* Design, train, fine-tune, and deploy ML/LLM models for production.

* Buid retrieval-augmented generation pipelines using vector databases and frameworks like LangChain, LangGraph, and MCP.

* Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions.

* Collaborate with data engineers to maintain data pipelines for ML workloads.

* Conduct feature engineering and embeddings generation on structured and unstructured data.

* Implement model monitoring, drift detection, and retraining pipelines.

* Explore emerging LLM/SLM architectures and multi-agent orchestration patterns.

* Cross-functional collaboration with R&D, data science, product, and engineering teams.

* Mentor junior engineers and contribute to best practices in ML engineering.




Requirements:



* Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.

* At least 3 years of experience building and deploying ML systems.

* English proficiency (B2 level).

* Strong Python skills and experience with PyTorch, TensorFlow, Scikit-Learn, Hugging Face Transformers.

  • * Familiarity with at least two of the following: OpenAI GPT, Anthropic Claude, Google Gemini, Meta LLaMA.

    * Knowledge of vector databases, embeddings, and RAG pipelines.

    * Experience working with both structured and unstructured data at scale.

    * Understanding of SQL and distributed data frameworks like Spark or Ray.

    * Deep knowledge of ML lifecycle: data preparation, training, evaluation, deployment, and monitoring.




    Preferred Qualifications:



    * Experience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).

    * Understanding of AI safety, guardrails, and explainability.

    * Hands-on experience deploying ML/LLM solutions on AWS, GCP, or Azure.

    * Familiarity with MLOps practices including CI/CD, monitoring, and observability.

    * Background in anomaly detection, fraud/risk modeling, or behavioral analytics.

    * Contributions to open-source AI/ML projects or research publications.



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