AI Engineer

Responsibilities

• Build and deploy agentic AI systems capable of autonomous decision-making, tool use, and multi-step task execution
• Implement end-to-end AI/ML and GenAI projects, from understanding business needs to data preparation, model development, deployment and monitoring
• Develop LLM-based features such as retrieval-augmented generation (RAG) with citations, text summarization, and embedding pipelines
• Design and optimize prompts using prompt engineering techniques for LLMs to achieve desired outcomes
• Work with Large Language Models (LLMs) such as Claude, GPT, Gemini, Llama, etc. via APIs or cloud AI platforms to develop solutions for specific tasks
• Evaluate and test GenAI features: building test sets, grounding and citation checks, LLM-as-judge scoring, and production quality monitoring
• Design, develop, and optimize machine learning models using Python
• Deploy and manage solutions in distributed and cloud environments
• Collaborate with cross-functional teams to guide business decisions

Job Requirements

• Bachelor's/Master's degree in CS, Data Science, Engineering, or Mathematics field
• 2+ years of hands-on AI/ML engineering experience, including demonstrable LLM application work
• Experience building agentic AI systems (agents with tool/function calling, planning or task decomposition, and multi-step execution), or strong working knowledge of agent architectures and frameworks such as LangGraph, CrewAI, Strands, or AutoGen
• Working knowledge of the modern LLM stack: prompt engineering, RAG, embeddings, and structured outputs
• Experience in one or more areas of machine learning / artificial intelligence such as classification, clustering, anomaly detection, sentiment analysis, and NLP problems such as text categorization, topic modeling, entity extraction, and text summarization
• Ability to think critically about AI or ML system design, including model selection, tradeoffs, and real-world deployment considerations
• Experience evaluating AI/ML systems: testing, measuring accuracy, and catching hallucinations
• Programming experience using Python and iPython notebooks; good SQL skills
• Excellent communication skills to communicate with wide technical and business users
• Demonstrate ability to quickly learn new tools and paradigms to deploy cutting edge solutions
• Adept at simultaneously working on multiple projects, meeting deadlines, and managing expectations

Preferred Skills

• Experience with prompt engineering techniques such as few-shot learning, zero-shot learning, and chain-of-thought prompting
• Experience with cloud platforms (AWS or Azure) and their AI/ML services such as AWS Bedrock, AWS SageMaker, Azure OpenAI, or Azure AI Foundry, and core services such as S3 and Lambda functions
• Experience in using deep learning frameworks such as PyTorch or Keras, etc.
• Experience in MLOps to operationalize the model building process and monitor models in production
• Familiarity with search and vector retrieval such as Elasticsearch, Solr, or vector databases
• Familiarity with version control systems, specifically Git, and experience with platforms like Azure DevOps
• Familiarity with Linux and cloud CLI tools
• Experience creating interactive data visualizations and dashboards in Tableau, Power BI, or other tools
• Experience with distributed NoSQL databases such as MongoDB, DynamoDB, etc.
• Ability to build full stack systems architected for speed and distributed computing.