Key skills
AWSETLPostgresPythonAIMachine LearningMLNLPGenerative AIGenAILLMLarge Language ModelsRAGMLOpsLangGraphPineconeDatabricksLambdaS3BedrockPostgreSQLCI/CD
About this role
Role Overview
- Collaborate with Colleagues – Work closely with colleagues to understand customers' business objectives and technical challenges, contributing to the design and development of effective GenAI solutions tailored to client needs.
- Apply GenAI Principles – Utilize modern tools and frameworks like LangGraph, to build scalable, reliable, and maintainable Compound AI systems.
- Leverage your understanding of AI fundamentals to ensure every project meets rigorous industry and ethical standards.
- Adapt to the latest Technologies & Patterns – continue to research, learn, and stay abreast of the most recent state of the art for AI application development.
- Promote Knowledge Sharing –Bolster our culture of continuous learning by sharing knowledge about AI engineering best practices through blog posts, articles, and internal talks. Support a collaborative environment that fosters shared expertise and ongoing innovation across our community.
Requirements
- 2+ years of experience in AI engineering, machine learning (ML), or related fields
- Generative AI (GenAI expertise)
- Strong understanding of state of the art techniques in generative AI, including large language models (LLMs), text generation and other foundation models
- Familiarity with AI orchestration tools (e.g. LangGraph, CrewAI, Bedrock Agents, smolagents, etc)
- Experience in fine-tuning, prompt engineering or otherwise adapting generative models for specific use cases
- Experience with AI model evaluation, including human-in-the-loop and LLM judge paradigms
- Familiarity with NLP libraries and frameworks
- Hands-on experience in implementing Retrieval Augmented Generation (RAG) architectures and integrating retrieval systems with generative models
- Knowledge of at least one vector store or database (e.g. Opensearch, Pinecone, PostgreSQL with pgvector) and techniques for similarity search
- Familiarity with common data ingestion/ETL patterns for populating knowledge bases
- Experience with implementing LLM tool calling (either directly, via an orchestration framework, or using Model Context Protocol (MCP) clients)
- Experience using Amazon Bedrock or Databricks AI for deploying and managing generative AI models
- Ability to integrate Generative AI scripts with other services (such as AWS S3 or Lambda or Databricks Apps) to build scalable and secure AI solutions
- Strong programming skills in Python (or similar languages)
- Familiarity with CI/CD pipelines and MLOps practices to ensure seamless integration, testing and deployment of AI models
Tech Stack
- AWS
- ETL
- Postgres
- Python
Benefits
- Flexible work arrangements
- Professional development opportunities