?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.
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.
This position is open to all candidates.
