Logo Provectus

Senior ML Engineer (GenAI, AWS)

Provectusvia Jobgether
RemotoUsSêniorCLT5 dias atrás

Salário Estimado

R$ 12.870,00 - R$ 19.305,00

Tecnologias

PythonGoAWSGCPDockerMachine LearningIATensorFlowPyTorch
0de 100

Excelente

Score da Vaga

Descrição da Vaga

This a Full Remote job, the offer is available from: Spain, Colombia, California (USA) Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value.


The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses.


As an ML Engineer, you’ll be provided with all opportunities for development and growth.


Let's work together to build a better future for everyone! Responsibilities:

•Technical Delivery (60%)- Design and implement end-to-end ML solutions from experimentation to production; - Build scalable ML pipelines and infrastructure; - Optimize model performance, efficiency, and reliability; - Write clean, maintainable, production-quality code; - Conduct rigorous experimentation and model evaluation; - Troubleshoot and resolve complex technical challenges.
•Collaboration and Contribution (25%);- Mentor junior and mid-level ML engineers; - Conduct code reviews and provide constructive feedback; - Share knowledge through documentation, presentations, and workshops; - Collaborate with cross-functional teams (DevOps, Data Engineering, SAs); - Contribute to internal ML practice development. • Innovation and Growth (15%)- Stay current with ML research and emerging technologies; - Propose improvements to existing solutions and processes; - Contribute to the development of reusable ML accelerators; - Participate in technical discussions and architectural decisions.

Requirements:

•Machine Learning Core- ML Fundamentals: supervised, unsupervised, and reinforcement learning; - Model Development: feature engineering, model training, evaluation, hyperparameter tuning, and validation; - ML Frameworks: classical ML libraries, TensorFlow, PyTorch, or similar frameworks; - Deep Learning: CNNs, RNNs, Transformers.
•LLMs and Generative AI- LLM Applications: Experience building production LLM-based applications; - Prompt Engineering: Ability to design effective prompts and chain-of-thought strategies; - RAG Systems: Experience building retrieval-augmented generation architectures; - Vector Databases: Familiarity with embedding models and vector search; - LLM Evaluation: Experience with evaluation metrics and techniques for LLM outputs. • Data and Programming- Python: Advanced proficiency in Python for ML applications; - Data Manipulation: Expert with pandas, numpy, and data processing libraries; - SQL: Ability to work with structured data and databases; - Data Pipelines: Experience building ETL/ELT pipelines - Big Data: Experience with Spark or similar distributed computing frameworks.
•MLOps and Production- Model Deployment: Experience deploying ML models to production environments; - Containerization: Proficiency with Docker and container orchestration; - CI/CD: Understanding of continuous integration and deployment for ML; - Monitoring: Experience with model monitoring and observability; - Experiment Tracking: Familiarity with MLflow, Weights and Biases, or similar tools. • Cloud and Infrastructure- AWS Services: Strong experience with AWS ML services (SageMaker, Lambda, etc.); -GCP Expertise: Advanced knowledge of GCP ML and data services; - Cloud Architecture: Understanding of cloud-native ML architectures;
•- Infrastructure as Code: Experience with Terraform, CloudFormation, or similar.

Will be a plus:

•Practical experience with cloud platforms (AWS stack is preferred, e.g.

Amazon SageMaker, ECR, EMR, S3, AWS Lambda); • Practical experience with deep learning models;

•Experience with taxonomies or ontologies; • Practical experience with machine learning pipelines to orchestrate complicated workflows;
•Practical experience with Spark/Dask, Great Expectations.

What We Offer:

•Long-term B2B collaboration;
•Fully remote setup; • A budget for your medical insurance;
•Paid sick leave, vacation, public holidays; • Continuous learning support, including unlimited AWS certification sponsorship.

Interview stages:

•Recruitment Interview;
•Tech interview; • HR Interview;
•HM Interview.

This offer from "Provectus" has been enriched by Jobgether.com and got a 81% flex score.

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Informações

NívelSênior
ContratoCLT
LocalUs
RemotoSim
MoedaBRL
Publicada5 dias atrás
FonteJobgether

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