What are the responsibilities and job description for the Senior AI Engineer position at SWITS DIGITAL Private Limited?
Title: Senior AI Engineer
Location: Orlando, FL (hybrid)
Job Description
Minimum Education and/or Experience:
Location: Orlando, FL (hybrid)
Job Description
- Evaluate AI Solutions: Analyze and benchmark AI platforms, LLMs, and agent frameworks for enterprise use.
- Build Agentic Models: Design and develop AI agents capable of reasoning, planning, and executing multi-step tasks.
- Engineer LLM Applications: Develop fine-tuned and optimized large language model applications with retrieval-augmented generation (RAG) pipelines.
- Integrate with Enterprise Platforms: Embed AI capabilities into OMS, CRM, loyalty, and digital platforms, ensuring seamless user and operational experiences.
- Establish AI Engineering Practices: Define standards for AI model deployment, observability, safety, and compliance.
- Collaborate Across Teams: Partner with Product, Digital Engineering, and Data Science to co-create AI-powered features and workflows.
- Governance & Security: Implement guardrails, privacy protections, and compliance controls for AI-enabled applications.
- Lead & Mentor: Guide a team of AI engineers and contribute to the enterprise AI strategy while remaining hands-on in technical delivery.
Minimum Education and/or Experience:
- Bachelor’s degree in Computer Science, Engineering, or related field; Master’s or PhD preferred.
- 8 years of professional experience in software engineering, AI/ML, or data platforms.
- Proven expertise in building and deploying LLM-powered applications and agentic AI models.
- Hands-on experience with RAG pipelines, embeddings, and vector search in production.
- Strong understanding of cloud-native AI services (Azure preferred).
- Experience guiding teams, delivering complex AI projects, and driving innovation.
- Experience with multimodal AI (vision, speech, structured data).
- Background in AI governance, responsible AI, or applied ethics.
- Publications, patents, or open-source contributions in AI/ML.
- Familiarity with aviation, logistics, or operational technology domains.
- Background in regulated industries (aviation, logistics, finance, or hospitality).
- AI/ML Frameworks: LangChain, LlamaIndex, Semantic Kernel, Hugging Face.
- LLMs: OpenAI GPT, Anthropic Claude, LLaMA, Falcon, or similar.
- Vector Databases: Pinecone, Weaviate, Redis Vector, or Azure Cognitive Search.
- Programming Languages: Python (preferred), TypeScript/Node.js for integrations.
- Cloud Platforms: Azure AI/ML services, Azure OpenAI, AWS Bedrock, or GCP Vertex AI.
- MLOps: MLflow, Azure Machine Learning, or equivalent model lifecycle tools.
- Observability: Langfuse, Weights & Biases, Application Insights for model monitoring.
- Security: OAuth 2.0, RBAC, data privacy, compliance frameworks (GDPR, PCI)
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