Project Description
We are seeking an experienced AI Engineer to design and deliver scalable GenAI solutions. This subcontractor role focuses on building production�?ready AI products using Python, React, and AWS, while leveraging modern architectures such as LLM pipelines, RAG, and agent�?based systems.
Responsibilities
AI Engineering
- Lead and contribute to the development of AI products, pilots, and solutions, emphasizing clean, maintainable code in Python, React, and AWS.
- Design, architect, and build scalable GenAI systems, including LLM pipelines, agentic architectures, MCP, Graph/RAG, and prompt�?based applications.
- Implement cloud�?native solutions using AWS services such as EKS, Lambda, Fargate, Glue, and Athena.
- Optimize performance of AI products and drive continuous experimentation with emerging GenAI methods, frameworks, APIs, and toolchains.
- Collaborate with product managers, data scientists, and domain experts to define technical solutions aligned with business needs.
- Serve as a GenAI SME, helping shape the organization's AI roadmap.
- Own end�?to�?end delivery of GenAI solutions, managing timelines, deliverables, and milestones using Agile (Scrum/Kanban).
- Monitor operational metrics and incident data to support continuous improvement and reliability.
- Ensure adherence to governance, DevSecOps, and security protocols.
Required Skills & Experience
Must�?Have Qualifications
- 6+ years of progressive engineering experience, including 1-2 years leading emerging tech or AI initiatives.
- Hands�?on experience with GenAI models (GPT, Claude, Gemini, LLaMA) and prompt engineering.
- Expertise in agentic AI, MCP, and Graph/RAG architectures.
- Proficiency with GenAI frameworks: LangChain, LlamaIndex, Amazon Bedrock.
- Strong web development experience using Next.js, React, TypeScript/JavaScript.
- Deep knowledge of AWS services: EC2, ELB/GLB/NLB, EKS, Fargate, Lambda, Athena, Glue, Lake Formation.
- Experience with IaC and containerization: Puppet, Terraform, Docker.
- ETL orchestration using Apache Airflow/DAGs.
- Familiarity with vector/graph databases: Weaviate, Milvus, PGVector, Neo4j, Neptune, including query optimization.
- Strong Python skills: NumPy, Pandas, Matplotlib, Boto3.
- Experience with automated testing frameworks: Ragas, Playwright, Zephyr, Selenium.
- Knowledge of SDLC best practices, DevSecOps, Agile (Scrum/Kanban), and work management tools ( JIRA, Confluence, JIRA Align).
- Understanding of LLM fine�?tuning techniques.
- Experience with BI tools: QuickSight, Tableau.
- Knowledge of financial markets and enterprise data systems.