Job Description: Artificial intelligence is transforming the world, and in this journey of innovation, we need a key talent to help us build intelligent solutions that truly make an impact. We are looking for a Machine Learning Engineer, a professional passionate about generative AI, capable of transforming concepts into functional realities. This position is not just technical; it is strategic. You will bridge theory and practice, working with generative models and vector databases to create intelligent agents that solve real-world problems. This role is essential for our client, a technology company advancing its solutions through the integration of advanced AI. If you are excited about developing technology that learns, adapts, and evolves continuously, this is your opportunity. Responsibilities: In this role, you will drive the transformation of data into practical, scalable solutions. 1. Model Development and Training: - Develop generative AI agents using frameworks like LangChain or CrewAI, integrating advanced techniques such as RAG (Retrieval-Augmented Generation). - Implement workflows to automate complex tasks, from data ingestion to prediction and decision-making. 2. Data Preprocessing and Management: - Build and optimize data pipelines using Python, Bash, and SQL, ensuring high-quality and consistent datasets. - Implement and query vector databases, such as FAISS, ChromaDB, or Pinecone, facilitating efficient information retrieval in generative AI environments. - Develop and maintain large-scale data processing systems with low-latency performance. 3. Deployment and Implementation: - Deploy models and AI agents in production environments, leveraging cloud services like AWS Lambda, S3, RDS, and API Gateway. - Integrate models into applications via APIs, ensuring scalability and availability. - Apply DevOps practices, including CI/CD automation for Software development. 4. Evaluation and Monitoring: - Continuously evaluate model performance using objective metrics, such as precision, recall, and F1 score. - Monitor production models to identify bias, performance degradation, and retraining needs. 5. Optimization and Maintenance: - Optimize model architecture to improve efficiency and reduce resource consumption. - Implement advanced techniques such as quantization, pruning, and parallelization, enhancing inference times and scalability. - Ensure code integrity using Git, applying best practices for version control and collaboration. 6. Collaboration and Communication: - Collaborate closely with Data Scientists, Software Engineers, and business teams to translate requirements into technical solutions. - Document models, workflows, and processes, ensuring project traceability and reproducibility. - Participate in ideation sessions and present results, continuously proposing improvements based on data insights. Required Skills: - Languages: Python, Bash, and SQL. - Frameworks: LangChain or CrewAI, with expertise in RAG techniques. - Databases: FAISS, OpenSearch, Pinecone. - Cloud: Advanced experience with AWS (Lambda, S3, RDS, API Gateway). - Version Control: Git, following best practices for branching strategies. - Model Evaluation: Proficient in implementing objective metrics to assess model performance. - Critical thinking and problem-solving mindset. - Ability to iterate quickly and test solutions based on data insights. - Autonomy, proactivity, and strong technical communication.