AI/ML Engineer – LLM & RAG Solutions

Role Overview:

We are seeking a skilled AI/ML Engineer with hands-on experience building and deploying LLM-powered applications in production environments. The ideal candidate will have expertise in prompt engineering, model versioning, output validation, fallback mechanisms, and Retrieval-Augmented Generation (RAG) frameworks.

The role requires experience designing secure and scalable AI solutions with policy-based input/output controls, ensuring reliability, compliance, and high-quality AI responses.

Key Responsibilities

• Design, develop, and deploy LLM-based applications and AI features in production environments
• Build and optimize RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases
• Implement:
  • prompt engineering strategies
  • prompt/version management
  • response validation
  • fallback and retry mechanisms
• Develop controlled generation workflows with:
  • input filtering
  • output moderation
  • policy enforcement
• Integrate AI models with APIs, vector databases, and enterprise applications
• Collaborate with product, engineering, and data teams to deliver scalable AI solutions
• Monitor model performance, hallucinations, latency, and response quality
• Improve reliability, observability, and governance of AI systems
• Contribute to AI architecture, experimentation, and optimization initiatives

Required Skills & Qualifications

• 3+ years of experience building and deploying AI/ML solutions
• Hands-on experience shipping LLM features into production
• Strong expertise in:
  • Prompt Engineering
  • Prompt/Model Versioning
  • Output Validation
  • Fallback Handling
• Experience with RAG architectures and semantic retrieval systems
• Knowledge of policy-based AI controls for:
  • input validation
  • output filtering
  • safe AI responses
• Strong programming skills in Python
• Experience with:
  • LangChain / LlamaIndex
  • OpenAI / Anthropic / Gemini APIs
  • Vector databases (Pinecone, Weaviate, Chroma, pgvector)
• Familiarity with cloud platforms such as AWS, Azure, or GCP

Preferred Skills

• Experience with AI observability and evaluation frameworks
• Exposure to guardrails, hallucination mitigation, and AI governance
• Knowledge of CI/CD and MLOps practices
• Experience working in enterprise or regulated environments