We are looking for a highly skilled Artificial Intelligence (AI) / Machine Learning (ML) Engineer with expertise in building AI-powered applications. We will be building AI & GenAI solutions end-to-end: from concept, through prototyping, production, to operations.

Artificial Intelligence / Machine Learning Engineer

Your responsibilities

• Generative AI Application Development: Collaborate with developers and stakeholders in Agile teams to integrate LLMs and classical AI techniques into end-user applications, focusing on user experience, and real-time performance
• Algorithm Development: Design, develop, customize, optimize, and fine-tune LLM-based and other AI-infused algorithms tailored to specific use cases such as text generation, summarization, information extraction, chatbots, AI agents, code generation, document analysis, sentiment analysis, data analysis, etc.
• LLM Fine-Tuning and Customization: Fine-tune pre-trained LLMs to specific business needs, leveraging prompt engineering, transfer learning, and few-shot techniques to enhance model performance in real-world scenarios
• End-to-End Pipeline Development: Build and maintain production-ready end-to-end ML pipelines, including data ingestion, preprocessing, training, evaluation, deployment, and monitoring; automate workflows using MLOps best practices to ensure scalability and efficiency
• Performance Optimization: Optimize model inference speed, reduce latency, and manage resource usage across cloud services and GPU/TPU architectures
• Scalable Model Deployment: Collaborate with other developers to deploy models at scale, using cloud-based infrastructure (AWS, Azure) and ensuring high availability and fault tolerance
• Monitoring and Maintenance: Implement continuous monitoring and refining strategies for deployed models, using feedback loops and e.g. incremental fine-tuning to ensure ongoing accuracy and reliability; address drifts and biases as they arise
• Software Development: Apply software development best practices, including writing unit tests, configuring CI/CD pipelines, containerizing applications, prompt engineering and setting up APIs; ensure robust logging, experiment tracking, and model monitoring

Our requirements

• Experience: 3+ years of experience in AI/ML engineering, with exposure to both classical machine learning methods and language model-based applications
• Technical Skills: Advanced proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow; expertise with Transformer architectures; hands-on experience with LangChain or similar LLM frameworks; experience with designing end-to-end RAG systems using state of the art orchestration frameworks (hands on experience with fine-tuning LLMs for specific tasks and use cases considered as an additional advantage)
• MLOps Knowledge: Strong understanding of MLOps tools and practices, including version control, CI/CD pipelines, containerization, orchestration, Infrastructure as Code, automated deployment
• Deployment: Experience in deploying LLM and other AI models with cloud platforms (AWS, Azure) and machine learning workbenches for robust and scalable productization
• Practical overview and experience with AWS services to design cloud solutions, familiarity with Azure is a plus; experience with working with GenAI specific services like Azure OpenAI, Amazon Bedrock, Amazon SageMaker JumpStart, etc.
• Data Engineering: Expertise in working with structured and unstructured data, including data cleaning, feature engineering with data stores like vector, relational, NoSQL databases and data lakes through APIs
• Model Evaluation and Metrics: Proficiency in evaluating both classical ML models and LLMs using relevant metrics
• Optimization Techniques: Experience with optimizing models for performance
• Problem-Solving Skills: Strong analytical skills with the ability to tackle complex engineering challenges, integrate new technologies, and improve existing processes.