What are the responsibilities and job description for the Sr. AI Developer position at FleetPride?
We are seeking a Senior AI Developer who operates at the intersection of deep technical execution and business impact. This role is 70% hands-on AI solution development and 30% business-facing leadership, translating complex AI capabilities into measurable enterprise value.
The ideal candidate builds production-grade AI systems — not prototypes that sit in notebooks. They partner directly with business leaders to design, explain, and implement AI solutions that drive revenue growth, cost efficiency, risk reduction, and operational velocity.
Responsibilities:
70% – Hands-On AI Development & Engineering
- Design, build, and deploy scalable AI/ML solutions (predictive models, generative AI, NLP, optimization, automation)
- Architect production-ready pipelines from data ingestion through model monitoring
- Develop and fine-tune LLM-based applications (RAG architectures, prompt engineering, agents, copilots)
- Write high-quality, production-grade code (Python required; additional languages a plus)
- Implement MLOps best practices (CI/CD, model versioning, monitoring, drift detection)
- Work across cloud platforms (Azure, AWS, or GCP) to deploy secure, enterprise-grade solutions
- Ensure governance, security, explainability, and responsible AI principles are embedded in every solution
- Optimize performance, scalability, and cost-efficiency of AI workloads
30% – Business Partnership & Solution Leadership
- Translate ambiguous business problems into structured AI solution designs
- Partner with business stakeholders (Finance, Operations, Sales, Marketing, IT) to identify high-value use cases
- Clearly explain AI concepts, tradeoffs, and model outputs to non-technical audiences
- Lead solution design workshops and whiteboarding sessions
- Quantify expected ROI and define measurable success metrics
- Influence prioritization of AI initiatives based on business value and feasibility
- Mentor junior developers and help elevate AI literacy across the organization
What Success Looks Like
- AI solutions deployed into production that generate measurable business impact
- Reduced time-to-value from idea to implementation
- Business leaders who trust and understand the AI solutions being delivered
- Scalable architecture that enables repeatable AI delivery across functions
Required Qualifications
- 7 years of software development experience
- 3 years building and deploying machine learning or AI solutions in production
- Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn)
- Experience with LLM ecosystems (OpenAI APIs, Hugging Face, LangChain, vector databases)
- Solid understanding of data engineering fundamentals
- Experience with cloud-native AI deployment (Azure ML, AWS SageMaker, Vertex AI, etc.)
- Ability to communicate complex technical ideas clearly and confidently
- Demonstrated experience working directly with business stakeholders
Preferred Qualifications
- Experience implementing enterprise-scale AI governance frameworks
- Background in analytics, optimization, or decision intelligence
- Experience designing AI copilots or intelligent automation solutions
- MBA or strong business acumen
- Experience in regulated or complex operational environments
Competencies
- Builder mindset with production discipline
- Systems thinker who designs for scale
- Commercially oriented technologist
- Comfortable in ambiguity
- Strong executive communication skills
- Bias for measurable outcomes over experimentation