AI/ML Engineer

We're looking for an AI/ML Engineer with experience building and deploying intelligent systems, spanning classical ML, deep learning, statistics, and modern Generative AI, across the full solution lifecycle from model development to production deployment.

Responsibilities

• Design, develop, and maintain ML/AI solutions using Python
• Build and deploy GenAI systems: LLMs, RAG pipelines, and agentic workflows (LangChain, LlamaIndex, LangGraph)
• Work with embeddings, semantic search, vector databases, and MCP-based tool integrations
• Develop and integrate RESTful APIs for AI/ML services
• Fine-tune and evaluate models (including LoRA/QLoRA) and build evaluation frameworks for accuracy, reliability, and bias
• Optimize for performance, latency, cost, and scalability
• Collaborate cross-functionally, write clean/testable code, participate in code reviews
• Stay current with new model releases, techniques, and tools, and judge what's actually worth adopting vs. hype
  
  

Requirements

• 3+ years in AI/ML development, strong Python
• Solid grasp of classical ML (regression, classification, clustering) and foundational statistics (probability, hypothesis testing, A/B testing)
• Understanding of deep learning concepts (neural network architectures, CNNs, RNNs, transformers, backpropagation, training/optimization techniques)
• Hands-on with NLP, Generative AI, embeddings, semantic search
• Experience with RAG, vector databases (Pinecone/Weaviate/Qdrant), and PostgreSQL/MongoDB
• Experience with agentic frameworks (LangChain, LangGraph, LlamaIndex) and MCP
• Familiarity with Git, CI/CD, Agile
• Experience with OpenAI, Anthropic, or similar LLM APIs
• Bonus: LoRA/QLoRA fine-tuning, reasoning/test-time-compute techniques, LLMOps
  
  

Stand out if you can

• Have taken at least one GenAI project end-to-end, from idea to a live, hosted deployment someone else can actually use (a multi-agent system, an AI product, or any other practical application), not just notebooks or coursework
• Demonstrate awareness of current AI trends and practical judgment on what's worth adopting
• Show sound reasoning around model selection and architecture tradeoffs for a given use case