[Hiring] Senior AI Engineer @INSPYR Solutions

Role Description

The Senior AI Engineer reports directly to the VP of AI and is responsible for leading and advancing enterprise-wide AI initiatives. This role leads the design, development, and deployment of end-to-end AI systems and intelligent agents that drive automation, decision-making, and business value at enterprise scale.

• Design, build, and deploy end-to-end AI systems — from data ingestion and model development through inference, monitoring, and continuous improvement.
• Architect and develop AI agents and multi-agent frameworks capable of reasoning, planning, and executing complex workflows autonomously.
• Build cohesive AI solutions through the orchestration and integration of Models, LLMs, agentic services, expert systems, and knowledge graphs.
• Leverage AI coding assistants (Claude Code, GitHub Codex, and similar tools) to accelerate development, automate repetitive engineering tasks, and improve code quality across the team.
• Build and maintain scalable AI pipelines on Databricks and AWS, integrating with existing data infrastructure and enterprise systems.
• Define and implement enterprise AI architecture standards, patterns, and best practices across the organization.
• Evaluate and integrate large language models (LLMs), foundation models, and generative AI capabilities into business applications.
• Collaborate with Data Scientists to operationalize ML models and move experiments from prototype to production.
• Partner with cross-functional teams across multiple simultaneous initiatives to scope, design, and deliver AI-powered solutions.
• Establish model monitoring, evaluation, and feedback loops to ensure AI systems remain accurate, safe, and performant in production.
• Stay current with the rapidly evolving AI landscape and proactively recommend new tools, frameworks, and approaches that improve outcomes.
• Mentor junior engineers and contribute to a culture of technical excellence, experimentation, and continuous learning.
• Prepare technical documentation, architecture diagrams, and executive presentations to communicate AI strategy and results.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related field; master's degree preferred.
• 7+ years of experience in software or data engineering with at least 5 years focused on AI/ML systems development.
• Demonstrated end-to-end experience building and deploying AI systems and AI agents in production environments.
• Proficiency with AI coding assistants such as Claude Code, GitHub Codex, or equivalent tools as part of an active development workflow.
• Hands-on experience with Databricks for model training, feature engineering, and pipeline orchestration.
• Solid experience with AWS cloud services (SageMaker, Lambda, S3, EC2, Step Functions, or equivalent) for AI/ML workloads.
• Strong Python skills including SparkSQL, MLlib, PyTorch, spaCy, and NLTK for NLP and ML model development.
• Experience integrating AI systems via REST APIs, GraphQL, and OAuth for secure, scalable enterprise connectivity.
• Proven ability to operate as a senior independent contributor across multiple initiatives simultaneously without close supervision.
• Experience designing enterprise AI architecture including APIs, orchestration layers, vector databases, and model serving infrastructure.

Preferred Skills

• Experience building multi-agent systems and knowledge graphs using frameworks such as LangGraph, AutoGen, CrewAI, or the Anthropic Agent SDK.
• Familiarity with front-end and visualization technologies including React/Native, Figma, Dash or similar, and Bootstrap for building AI-powered user interfaces and data applications.
• Familiarity with prompt engineering, retrieval-augmented generation (RAG), and fine-tuning techniques for production LLM applications.
• Experience with MLOps practices including CI/CD for AI systems, model versioning, and automated evaluation pipelines.
• Knowledge of vector databases such as Pinecone, Weaviate, or pgvector for semantic search and retrieval applications.
• Familiarity with data governance, AI safety, and responsible AI principles in enterprise settings.
• Experience with Databricks Unity Catalog, Delta Lake, and MLflow for end-to-end model lifecycle management.
• Strong communication and stakeholder management skills — able to present technical AI concepts clearly to both engineering teams and business executives.
• Ability to evaluate build vs. buy tradeoffs for AI tooling and make architecture recommendations with long-term maintainability in mind.
• Experience contributing to AI strategy, roadmap planning, and organizational AI adoption initiatives.
• Attention to detail with a strong bias toward shipping reliable, well-documented, production-grade systems.