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 organization's 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 Next.js, 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