AI Developer

Artificial Intelligence Solutions Engineer

About the Opportunity

A forward-thinking organization is seeking an AI Solutions Engineer to help drive innovation through the design, development, and deployment of intelligent technologies across multiple business functions. This role will focus on leveraging machine learning, generative AI, and automation to streamline operations, enhance decision-making, and deliver measurable productivity gains.

The successful candidate will be responsible for transforming business challenges into scalable AI solutions, working closely with stakeholders to identify opportunities where emerging technologies can create meaningful impact.

Key Responsibilities

AI Product Development

• Design, develop, and implement AI-powered applications that improve operational efficiency and business performance.
• Build and deploy machine learning models and generative AI solutions that address real-world business challenges.
• Develop scalable architectures supporting training, evaluation, inference, and continuous model improvement.
• Create reusable AI frameworks and components that accelerate future solution delivery.

Generative AI & Intelligent Automation

• Engineer, test, and refine prompts to maximize accuracy, consistency, and business value.
• Develop retrieval-based AI applications utilizing embeddings, semantic search, and knowledge retrieval techniques.
• Implement intelligent automation solutions that reduce manual effort and improve workflow efficiency.
• Evaluate emerging AI technologies and identify opportunities to incorporate them into existing business processes.

Stakeholder Engagement

• Collaborate closely with business leaders and subject matter experts to understand operational challenges and identify high-impact opportunities for AI adoption.
• Translate business requirements into practical, scalable technical solutions.
• Present findings, recommendations, and technical concepts to both technical and non-technical audiences.
• Serve as a trusted advisor on AI capabilities, limitations, and implementation best practices.

Model Performance & Governance

• Establish evaluation methodologies to measure model effectiveness and business impact.
• Optimize solution performance through experimentation, testing, and iterative improvement.
• Ensure compliance with responsible AI principles, including data governance, security, transparency, and privacy considerations.
• Maintain documentation for models, implementation approaches, and solution architectures.

Technical Leadership

• Promote AI engineering standards and best practices across technology teams.
• Mentor team members on modern machine learning and generative AI methodologies.
• Contribute to the organization’s long-term AI strategy and innovation roadmap.
• Stay informed on advancements within artificial intelligence, machine learning, and intelligent automation.

Required Qualifications

• 5+ years of experience developing machine learning or artificial intelligence solutions within enterprise environments.
• Hands-on experience building and deploying generative AI applications.
• Strong knowledge of prompt engineering, retrieval-augmented generation (RAG), semantic search, and embeddings.
• Experience with AI development platforms such as Azure OpenAI, Azure Machine Learning, or comparable cloud AI services.
• Advanced proficiency in Python and experience building production-ready applications.
• Experience working with structured, semi-structured, and unstructured datasets.
• Proven ability to train, fine-tune, evaluate, and optimize models for business-focused use cases.
• Strong understanding of model evaluation methodologies and performance optimization techniques.
• Excellent analytical, problem-solving, and communication skills.
• Ability to effectively communicate technical concepts to business stakeholders.

Preferred Qualifications

• Experience working with vector databases and semantic search technologies.
• Familiarity with Databricks or large-scale data engineering ecosystems.
• Experience with AI orchestration frameworks such as LangChain or similar tools.
• Knowledge of MLOps, model lifecycle management, version control, and monitoring practices.
• Experience deploying AI solutions into enterprise production environments.
• Familiarity with automation technologies, cloud functions, and integration services.
• Understanding of enterprise data governance, securit…