Manager, AI Engineer (Python)

Manager AI Engineer - Python

As a Manager Technology you will lead and manage and hands on Python-based engineering teams ensuring the successful delivery of high-quality scalable and efficient solutions. You will work closely with cross-functional teams to drive technical excellence mentor engineers and contribute to the strategic direction of technology initiatives.

AI Engineer Responsibilities

• Lead and actively contribute to the development of AI products pilots and solutions with a focus on clean maintainable code using Python React and AWS tools.
• Design architect and build scalable Gen AI solutions including LLM pipelines Agentic MCP Graph/RAG architectures and prompt-based applications and emerging tech.
• Implement cloud-native solutions using AWS services such as EKS Lambda Fargate Glue and Athena.
• Optimize performance of AI products Drive continuous learning and experimentation with cutting-edge Gen AI methods frameworks APIs and toolchains.
• Work closely with product managers data scientists and domain experts to define technical solutions aligned with business needs.
• Act as a subject matter expert (SME) on Gen AI technologies and help shape the organizations AI roadmap.
• Own end-to-end delivery of Gen AI solutions. Manage timelines deliverables and project milestones using Agile practices (Scrum/Kanban).
• Monitor operational metrics and incident data to drive continuous improvement and reliability.
• Ensure adherence to governance DevSecOps protocols.
  

Experience/Skiils:

• 6 years of progressive experience in engineering roles including at least 1-2 years leading emerging tech or AI initiatives.
• Gen AI models (GPT Claude Gemini LLaMA) and prompt engineering techniques
• Agentic AI MCP and Graph/RAG architectures
• Gen AI Framework (LangChain LlamaIndex Amazon Bedrock)
• Web application development using React TypeScript/JavaScript
• AWS cloud services (EC2 ELB/GLB/NLB EKS Fargate Lambda Athena Glue Lake Formation)
• Infrastructure as Code (Puppet Terraform Docker) and containerized deployments
• ETL orchestration using Apache Airflow/DAGs
• Vector/Graph databases (Weaviate Milvus PGVector Neo4J Neptune) and query optimization
• Python programming (NumPy Pandas Matplotlib Boto3)
• Automated testing frameworks (Ragas Playwright Zephyr Selenium)
• Familiarity with SDLC best practices DevSecOps Agile Scrum/Kanban and work management tools (JIRA Confluence JIRA Align).
• Knowledge of LLM fine tuning techniques
• Experience in BI tools like QuickSight Tableau
• Knowledge of financial markets and enterprise data systems