About the Role
Join Infomineo as a Senior AI Software Engineer to design, develop, and deploy AI-powered data products and analytical applications. You will lead data science and AI initiatives, define best practices for applied AI solutions, and collaborate with engineering, product, and business stakeholders. This role combines data science expertise with full-stack development and cloud deployment capabilities.
Key Responsibilities
Data Science, AI & Applied R&D
- Lead the design and development of AI-powered analytical solutions and intelligent applications solving complex business problems
- Design, prototype, and productionize machine learning, LLM, and generative AI solutions with focus on business value and reliability
- Own the architecture of Retrieval-Augmented Generation (RAG) pipelines including document processing, vectorization, and semantic search
- Design and implement AI-powered features by integrating LLM APIs using frameworks such as LangChain with focus on production reliability
- Design, implement, and maintain Model Context Protocol (MCP) integrations connecting AI models with external tools and data sources
- Develop evaluation frameworks, monitoring approaches, and observability practices for LLM-powered systems
- Apply advanced prompt engineering, embedding strategies, and vector database management to improve AI solution performance
Full-Stack Application Development
- Design and develop production-grade AI and data applications using Python and backend frameworks such as FastAPI
- Build frontend interfaces using modern frameworks such as React, Next.js, or Vue for business users and clients
- Develop and maintain REST API integrations with third-party AI services, enterprise SaaS platforms, and external data sources
- Ensure data science prototypes are translated into maintainable, secure, and scalable production solutions
Cloud, Deployment & MLOps
- Support containerization and cloud deployment of AI applications on Google Cloud Platform using GKE and Artifact Registry
- Design and maintain CI/CD pipelines using GitHub Actions to ensure reliable and repeatable releases
- Apply MLOps and LLMOps practices to manage experimentation, deployment, monitoring, and continuous improvement
- Proactively identify performance bottlenecks in AI workflows, data pipelines, and infrastructure
Technical Leadership & Collaboration
- Lead applied AI and data science initiatives from discovery through production deployment
- Mentor junior data scientists, AI engineers, and developers on data science methods and production readiness
- Work closely with product teams and non-technical stakeholders to ensure solutions align with business needs
- Define standards and best practices for AI solution design, evaluation, and delivery
Required Qualifications
- 4 to 6 years of experience in data science, AI development, or applied machine learning with hands-on delivery of production-grade AI products
- Strong proficiency in Python with experience using data science, machine learning, and AI libraries
- Solid full-stack development background including FastAPI and modern frontend frameworks
- Deep understanding of LLMs, RAG architectures, generative AI workflows, and production AI service integration
- Proven experience designing and implementing Model Context Protocol (MCP) integrations
- Experience building analytical workflows, dashboards, data pipelines, or AI-powered decision-support tools
- Hands-on experience with Docker and cloud deployment on GCP, AWS, Azure, or equivalent
- Familiarity with container orchestration, CI/CD pipelines, GitHub Actions, and GitOps workflows
- Strong understanding of LLM observability, AI evaluation, and production reliability practices
- Demonstrated ability to lead technical initiatives and mentor junior team members
- Bachelor's or Master's degree in Data Science, Computer Science, Software Engineering, Statistics, or related field
Preferred Skills
- Experience with agentic AI frameworks such as LangGraph for building multi-step AI workflows
- Knowledge of advanced prompt engineering, vector database management, and embedding model optimization
- Experience with MLOps or LLMOps practices including experiment tracking and model monitoring
- Experience with Infrastructure as Code tools such as Terraform or Pulumi
- Relevant cloud and AI certifications such as Google Cloud Professional Data Engineer or Machine Learning Engineer
What We Offer
- Competitive compensation and benefits package
- Opportunity to lead AI and data science initiatives with global impact
- Dynamic work environment valuing leadership, innovation, and continuous learning
- Professional development opportunities in AI, data science, and technology
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