DevJobs

Generative AI Engineer

Overview
Skills
  • Python Python ꞏ 5y
  • Neo4j Neo4j
  • Microservices Microservices
  • CI/CD CI/CD
  • Git Git
  • AWS Lambda AWS Lambda
  • AWS S3
  • AWS SageMaker
  • Docker Docker
  • Haystack
  • Weaviate
  • Unit Testing
  • RAG
  • Pinecone
  • LlamaIndex
  • LangChain
  • ChromaDB
  • AWS Glue
  • AWS Bedrock
  • AWS Athena
  • APIs
  • Kubeflow
  • MLflow
  • Amazon Neptune
abra professional services is seeking a Senior Generative AI Engineer!

This is a hands-on role combining deep technical expertise with strategic thinking to design, develop, and implement advanced Generative AI solutions. The role offers significant business impact, transforming complex challenges into scalable AI-powered products and solutions used in large-scale production environments.

Key Responsibilities:

  • Lead the end-to-end architecture, design, development, and deployment of advanced Generative AI applications, including Multi-Agent Systems and Retrieval-Augmented Generation (RAG) solutions.
  • Design and optimize RAG pipelines, integrating Large Language Models (LLMs) with structured and unstructured data, including Knowledge Graphs and Vector Stores.
  • Build, manage, and automate Generative AI solutions using AWS services, including Bedrock, S3, SageMaker, Lambda, and Step Functions.
  • Lead Proof of Concepts (POCs) and evaluate emerging GenAI technologies and products to identify the most suitable solutions for business needs.
  • Integrate AI solutions with existing enterprise systems using APIs and Microservices.
  • Collaborate with Data Engineers, Analysts, and Product Managers to translate business requirements into scalable and practical AI solutions.
  • Implement advanced MLOps processes, including CI/CD, Docker, and monitoring solutions, to ensure the reliability, scalability, and maintainability of AI applications.

Requirements:

Requirements:

  • 5+ years of hands-on Python development experience, with expertise in building and deploying AI and Machine Learning applications – mandatory.
  • Extensive hands-on experience with AWS services relevant to AI/ML, including S3, Glue, Athena, SageMaker, Lambda, and Bedrock – mandatory.
  • Proven experience with Generative AI frameworks such as LangChain, LlamaIndex, or Haystack – mandatory.
  • Hands-on experience designing and deploying RAG-based applications, along with practical knowledge of Vector Databases such as Pinecone, Weaviate, or ChromaDB and document indexing techniques – mandatory.
  • Strong understanding of software development principles, including Git version control, clean code practices, and Unit Testing – mandatory.
  • Strong analytical and problem-solving skills, with the ability to break down complex business challenges into actionable technical solutions – mandatory.
  • Excellent communication and collaboration skills, with the ability to clearly explain technical concepts to both technical and non-technical stakeholders – mandatory.

Nice to Have:

  • Domain Knowledge: Previous experience in the financial services, fintech, or a related highly-regulated industry.
  • Database Experience: Experience with specialized databases such as graph databases (e.g., Neo4j, Amazon Neptune).
  • MLOps Tools: Familiarity with MLOps platforms beyond AWS, such as Kubeflow or MLflow.
  • Education: A Master's or Ph.D. in Computer Science, AI, or a related field.
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