Description

Project description

We are looking for an experienced AI Engineer to design and deliver scalable Gen AI solutions. The role involves building production-ready AI products using Python, React, and AWS, while leveraging modern architectures such as LLM pipelines, RAG, and agent-based systems.

This role is for subcontractors.

Responsibilities

  • 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.

Skills

Must have

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