Senior AI Engineer
Summary
Build production-ready Gen AI solutions using Python, React, AWS, LLM pipelines, RAG, and agent-based systems for scalable AI products.
- 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
- 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
Mandatory Skills Descriptio
- Experience/Skiil
- 6+ years of progressive experience in engineering roles, including at least 1-2 years leading emerging tech or AI initiative
- Gen AI models (GPT, Claude, Gemini, LLaMA) and prompt engineering techniqu
- Agentic AI, MCP, and Graph/RAG architectur
- AWS cloud services (EC2, ELB/GLB/NLB, EKS, Fargate, Lambda, Athena, Glue, Lake Formatio
- Infrastructure as Code (Puppet, Terraform, Docker) and containerized deploymen
- ETL orchestration using Apache Airflow/DA
- Vector/Graph databases (Weaviate, Milvus, PGVector, Neo4J, Neptune) and query optimizati
- 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 techniqu
- Knowledge of financial markets and enterprise data syste