Sr ML Engineer

Sr ML Engineer

Yalo

Hi! We’re Yalo! We’re on a mission to revolutionize how businesses sell in an omnichannel way with our intelligent sales platform and intelligent agents powered by cutting-edge AI. Imagine a world where businesses seamlessly connect with their customers across every channel—offering personalized experiences, anticipating needs, and delivering what they want with ease. That’s the reality we’re building at Yalo.

Born in Latin America and driven by its spirit of innovation, we’re transforming sales for businesses around the globe. From empowering businesses in emerging markets to helping enterprises scale intelligently, we’re redefining how companies engage with their customers and drive growth.

At Yalo, we believe the future of sales is personalized, omnichannel, intelligent, and conversational. Join us as we empower businesses to build stronger relationships and achieve remarkable results worldwide!

Job Summary 🧾

As a Sr Machine Learning Engineer, you will play a pivotal role in designing, developing, and deploying machine learning models and systems that power our AI-driven products. You will work closely with cross-functional teams, including data scientists, software engineers, and product managers, to deliver scalable and efficient ML solutions. Your expertise will be crucial in defining architectures, evaluating large language models (LLMs), building APIs to expose ML models, and conducting A/B testing experiments to validate model performance.

What are the responsibilities for this role? 🧠

• Model Development: Design, implement, and optimize machine learning models for AI chatbots, recommendation systems, and generative AI applications.
• Generative AI Architecture: Define and implement architectures for generative AI systems, including the integration of frameworks like Langchain, Langsmith, and Langgraph for building, evaluating, and monitoring LLM-based solutions.
• LLM Evaluation: Develop frameworks and methodologies for evaluating the performance, accuracy, and ethical compliance of LLM responses in production environments.
• API Development: Build robust and scalable APIs to expose machine learning models for integration with other systems and services.
• A/B Testing: Design and implement A/B testing experiments to evaluate the effectiveness of machine learning models and algorithms in real-world scenarios.
• System Architecture: Architect and build scalable, robust, and efficient ML systems and pipelines, leveraging Google Cloud Platform (GCP) for deployment and management.
• Deployment: Lead the deployment of machine learning models into production environments, ensuring reliability, scalability, and performance.
• Research and Innovation: Stay abreast of the latest advancements in machine learning, generative AI, and LLMs, and apply innovative techniques to improve model performance and capabilities.
• Collaboration: Work closely with cross-functional teams to understand business requirements and translate them into technical solutions.
• Mentorship: Provide guidance and mentorship to junior engineers, fostering a culture of continuous learning and improvement.
• Performance Monitoring: Implement monitoring and logging solutions to track model performance and identify areas for improvement.

Job Requirements?💻

• Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Machine Learning, Artificial Intelligence, or a related field.

• Experience: 5+ years of experience in machine learning, data science, or a related field, with a proven track record of delivering impactful ML solutions.

Technical Skills:

• Proficiency in programming languages such as Python, Java, or C++.
• Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with cloud platforms and their machine learning tools (ideally GCP).
• Familiarity with MongoDB and other NoSQL databases for production data storage.
• Solid understanding of software engineering principles and best practices.
• Familiarity with MLOps practices and tools for model deployment and monitoring.
• Problem-Solving: Strong analytical and problem-solving skills, with the ability to tackle complex and ambiguous challenges.

Soft Skills that matter to us🫀

• Communication: Excellent communication and interpersonal skills, with the ability to convey complex technical concepts to non-technical stakeholders.

• Leadership: Proven leadership skills, with the ability to mentor and guide junior engineers and influence technical direction.

Metrics to measure 📈

• Model Impact on Key Business Metrics: Measures the tangible business value delivered by the ML systems.
  • Examples: User engagement, Revenue, etc.

• System Reliability and Performance: Ensures that ML solutions are scalable, reliable, and meet user expectations.

  • Examples: Uptime and API response time. 

• Unlimited PTO policy
• Competitive rewards on the market range
• Remote working is available (-+3 hours CT)
• Flexible time (driven by results)
• Start-up environment
• International teamwork
• You and nothing else limit your career here

We care,
We keep it simple,
We make it happen,
We strive for excellence.