Join our remote-first culture as an AI Solution Engineer, a forward-looking, hands-on role where you will serve as a catalyst for intelligent transformation across the organization. You will partner with business leaders, product teams, and engineers to identify high-impact opportunities for AI adoption, design and implement practical solutions, and build the internal capability needed to sustain them. This role blends strategic thinking with technical execution you will move fluidly between discovery, prototyping, and delivery to drive measurable efficiency gains across the business.

Responsibilities

As an AI Solution Engineer, you will work across teams to surface, design, and deliver AI-driven improvements. Your focus will be on business impact, solution quality, and enabling others to build confidently with AI.

- Efficiency Discovery & Prospect Mapping: Conduct structured assessments across business units to identify manual, repetitive, or data-intensive workflows where AI can deliver measurable time and cost savings. Translate operational pain points into prioritized opportunity backlogs with clear ROI framing.
- AI Solution Design & Prototyping: Architect and build AI-powered solutions including LLM integrations, intelligent automation, and ML-assisted workflows from concept through production. Develop rapid prototypes to validate feasibility and demonstrate value before full-scale investment.
- Cross-Functional Stakeholder Partnership: Act as the technical bridge between business stakeholders, product management, and engineering teams. Translate ambiguous business requirements into concrete AI solution designs and communicate tradeoffs clearly to both technical and non-technical audiences.




- Implementation & Delivery: Own end-to-end delivery of AI solutions, including integration with existing systems, API design, prompt engineering, evaluation frameworks, and monitoring. Ensure solutions meet reliability, security, and scalability standards.
- AI Enablement & Internal Capability Building: Develop internal tooling, documentation, and training to help non-technical teams adopt AI tools effectively. Champion AI literacy across the organization and establish best practices for responsible, effective AI use.
- AI Governance & Responsible Deployment: Define and enforce standards for responsible AI deployment, including data privacy, bias mitigation, auditability, and compliance. Ensure all solutions align with organizational and regulatory requirements.

Qualifications

Core Experience

- 5+ years of professional experience in software engineering, data science, or a related technical discipline with a track record of shipping production systems.
- 2+ years of hands-on experience designing and deploying AI/ML solutions, including LLM-based applications, intelligent automation, or ML-assisted workflows.
- Demonstrated experience conducting process or workflow assessments to identify automation or AI opportunities across business functions.
- BS/MS in Computer Science, Engineering,



Mathematics, or equivalent practical experience.

Technical Expertise

- AI & LLM Development: Hands-on experience with LLM APIs (e.g., Claude, OpenAI, Gemini), prompt engineering, RAG architectures, and agent frameworks (e.g., LangChain, LlamaIndex).
- Cloud & Infrastructure: Proficiency with AWS or equivalent cloud platforms, including serverless architectures, managed AI/ML services (e.g., SageMaker, Bedrock), and Infrastructure as Code.
- Software Engineering: Strong proficiency in Python and/or Java; experience building APIs, data pipelines, and integrating AI components into existing software systems.
- Data & Analytics: Experience with SQL and NoSQL databases, data wrangling, and building evaluation frameworks to measure AI solution performance.

Leadership & Communication

- Proven ability to engage business stakeholders to surface workflow inefficiencies and translate them into scoped, deliverable AI initiatives.
- Strong written and verbal communication skills; able to present technical solutions and ROI narratives to executive, product, and engineering audiences.
- A pragmatic, outcome-oriented mindset comfortable navigating ambiguity and scoping solutions that balance innovation with practical constraints.
- Commitment to responsible AI principles: fairness, transparency, privacy, and auditability in all deployed solutions.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 AI Solution Engineer (India)
🏢 Cayuse
📍 India