Apply to 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