Note: The job is a remote job and is open to candidates in USA. Kavalio is seeking a highly skilled Senior AI/ML Engineer to lead the design, development, deployment, and optimization of advanced machine learning and artificial intelligence solutions. This role involves owning the full AI development lifecycle and collaborating with various teams to build scalable AI systems that drive innovation and business outcomes.
Responsibilities
- Design, build, train, evaluate, and deploy machine learning and AI solutions for complex business challenges
- Translate ambiguous requirements into scalable AI-driven systems and technical architectures
- Develop predictive models, classification systems, recommendation engines, personalization frameworks, and generative AI applications
- Fine-tune, evaluate, and optimize Large Language Models (LLMs) and other advanced AI architectures as appropriate
- Build robust, scalable machine learning pipelines from data ingestion through production deployment
- Implement CI/CD processes, automated testing, model validation, monitoring, and retraining workflows
- Develop production-ready APIs and services that integrate AI capabilities into enterprise applications
- Maintain model lifecycle management, performance monitoring, and continuous optimization processes
- Ensure systems meet security, scalability, reliability, and performance standards
- Document architectures, models, deployment procedures, and engineering standards to support maintainability and knowledge sharing
- Collaborate with cross-functional stakeholders to communicate technical solutions and recommendations
- Stay current with emerging AI technologies, frameworks, and industry best practices
- Contribute to AI strategy, platform selection, and architectural decision-making
Skills
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related technical discipline
- 5+ years of professional experience in machine learning, AI engineering, software engineering, or data science with production deployments
- 3+ years of experience building and deploying machine learning solutions in commercial environments
- Advanced Python programming skills with experience developing, training, testing, and deploying machine learning models
- Strong SQL expertise for data extraction, transformation, and analysis
- Experience building end-to-end ML systems from data ingestion through production serving
- Strong understanding of supervised, unsupervised, and deep learning methodologies
- Experience deploying solutions within cloud environments such as AWS, Azure, or Google Cloud Platform
- Familiarity with modern data platforms including Snowflake, BigQuery, Redshift, SQL Server, or equivalent technologies
- Demonstrated experience owning AI solutions in production environments including monitoring, optimization, and operational support
- Strong problem-solving, systems-thinking, and analytical skills
- Excellent communication skills with the ability to explain technical concepts to both technical and non-technical audiences
- Ability to manage multiple priorities in a fast-paced environment
- Must be available to start onsite and travel occasionally as required
- Master's degree or higher in Computer Science, Artificial Intelligence, Machine Learning, Statistics, Mathematics, or a related field
- Experience with MLOps platforms and orchestration tools such as MLflow, Airflow, SageMaker, Kubeflow, Databricks, or similar technologies
- Hands-on experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, and AI agents
- Experience working with Hugging Face, Bedrock, Azure AI Studio, OpenAI APIs, LangChain, LlamaIndex, or equivalent AI frameworks
- Knowledge of transformer architectures, NLP, embeddings, recommendation systems, and personalization engines
- Experience developing scalable AI applications using containerization and orchestration technologies such as Docker and Kubernetes
- Strong understanding of cloud-native AI infrastructure and distributed computing environments
- Experience designing AI governance, model monitoring, evaluation frameworks, and responsible AI practices
- Proven ability to influence technical strategy and architectural decisions for AI platforms and infrastructure
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