AI Engineer (LLMs & Generative AI)

• Design, develop, and deploy applications powered by LLMs and generative AI models.
  
• Build AI solutions for enterprise search, document intelligence, summarization, conversational AI, workflow automation, and knowledge management.
  
• Work with both commercial and open-source LLMs based on business and technical requirements.
  
• Optimize prompts, model parameters, inference pipelines, and latency/cost tradeoffs.
  
• Develop scalable APIs and backend services supporting AI-driven applications.
  

• Design and implement AI guardrails to ensure safe, reliable, and policy-compliant outputs.
  
• Develop mechanisms for:
  
• Hallucination mitigation
  
• Content filtering and moderation
  
• Prompt injection defense
  
• Output validation and verification
  
• Access and usage controls
  
• Build evaluation frameworks to measure safety, groundedness, consistency, accuracy, and model reliability.
  
• Partner with security and compliance teams to align AI systems with enterprise governance standards.
  

• Work with large-scale structured and unstructured enterprise datasets.
  
• Design and optimize Retrieval-Augmented Generation (RAG) pipelines.
  
• Build workflows for:
  
• Data ingestion and preprocessing
  
• Chunking and embeddings
  
• Vector indexing and semantic retrieval
  
• Context ranking and relevance optimization
  
• Collaborate with data engineering teams to ensure scalability and high performance across distributed systems.
  

• Implement orchestration strategies across multiple models and APIs.
  
• Develop fallback, routing, and hybrid model strategies to optimize performance and cost.
  
• Define and monitor evaluation metrics for model quality and reliability.
  
• Conduct benchmarking, A/B testing, and continuous optimization of AI systems.
  

• Build production-grade AI systems using modern software engineering best practices.
  
• Integrate AI services into enterprise applications, APIs, and workflows.
  
• Support CI/CD pipelines, testing, versioning, observability, and monitoring for AI platforms.
  
• Ensure systems are scalable, secure, observable, and cost-efficient.
  

• Partner with product, engineering, data, and business teams to translate requirements into AI-driven solutions.
  
• Communicate technical concepts clearly to global stakeholders.
  
• Contribute to architecture decisions, reusable AI frameworks, and technical documentation.
  
• Collaborate effectively in English-speaking international environments.
  

Requirements

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related field.
  
• 5+ years of experience in software engineering, AI, or machine learning roles.
  
• Strong hands-on experience building applications using LLMs and generative AI technologies.
  
• Experience working with large-scale enterprise data environments.
  
• Deep understanding of:
  Prompt engineering
  RAG architectures
  AI evaluation frameworks
  AI safety and guardrails
  Strong programming expertise in Python and backend development.
  Experience designing and deploying production-ready AI systems.
  Fluent English communication skills (required).
  Bilingual English/Spanish communication skills preferred.
  

• Experience with:
  
• LangChain
  
• LlamaIndex
  
• Semantic Kernel
  
• Hugging Face ecosystem
  
• OpenAI, Azure OpenAI, and Anthropic APIs
  
• Experience with vector databases such as:
  
• Pinecone
  
• Weaviate
  
• FAISS
  
• Knowledge of:
  
• Embeddings and semantic search
  
• Model fine-tuning and adaptation techniques
  
• AI observability and monitoring tools
  
• Familiarity with:
  
• AWS, Azure, or GCP
  
• Databricks
  
• Apache Spark
  
• Distributed data systems
  
• Docker and Kubernetes
  
• MLOps pipelines and AI lifecycle management
  
• Understanding of AI security, privacy, governance, and enterprise access controls.
  

• Microsoft Certified: Azure AI Engineer Associate
  
• AWS Certified Machine Learning – Specialty
  
• Google Professional Machine Learning Engineer
  
• Databricks Machine Learning Certification
  
• TensorFlow Developer Certificate
  
• Certifications in Responsible AI, AI Governance, or Data Engineering