Lead AWS Software Engineer (AI and DevOps SME)

Benefits

• Health benefits
• Life insurance
• Voluntary lifestyle benefits
• Other benefits and perks

Responsibilities

• Assess customer requirements autonomously, identifying and reconciling conflicting or complementary needs across stakeholder groups.
• Leverage deep expertise to design and develop software solutions that address customer needs.
• Lead design work using a structured, process driven approach.
• Roll out new software technologies and coordinate concurrent implementation tasks across teams.
• This position has a single opening and is based in the Reston, VA office.

Requirements

• Four years of experience in software engineering, preferably within financial services.
• Proficient in Python and SQL with AWS experience.
• Four years of DevOps engineering experience.
• Two to three years in AI/ML engineering roles.
• Understanding of financial data, KPIs, and reporting standards.
• Excellent communication and collaboration skills.
• Bachelor's degree in a related field is required.

Technologies

• AWS
• Python
• SQL
• REST
• SOAP
• FastAPI
• React
• Angular
• JavaScript
• TypeScript
• AJAX
• HTML5
• CSS3
• Streamlit
• Terraform
• CloudFormation
• Docker
• Kubernetes (EKS)
• Infrastructure-as-Code (IaC)
• Jenkins
• GitHub Actions
• GitLab CI
• AWS DevOps services
• AWS CloudWatch
• Splunk
• LangChain
• LangGraph
• AI agents
• RAG frameworks
• Vector databases
• Embedding models
• MCP integrations
• Power BI
• Tableau

Impact you will make

The Lead AWS Software Engineer (AI and DevOps SME) role offers flexibility and a collaborative environment to deliver on the responsibilities described above.

The Experience You Bring to the Team

Minimum Required Experiences

• 4 years overall in software engineering, preferably in financial services.
• Programming skills in Python, SQL and experience with AWS.
• 4 years DevOps engineering experience.
• 2-3 years of experience in AI/ML engineering roles.
• Understanding financial data, KPIs, and reporting standards.
• Excellent communication and collaboration skills.

Desired Experiences

• Experience in finance or fintech with exposure to building and managing Risk Management and Audit applications
• Experience with data visualization tools such as Power BI or Tableau

AWS and Cloud Engineering

• Experience leveraging AWS cloud services to design, develop, and support scalable, cloud-native enterprise applications and data platforms.
• Strong expertise in Python, SQL, and AWS for developing cloud-native applications, data pipelines, and scalable enterprise solutions.
• Designed and integrated enterprise applications using REST, SOAP, and Fast API services, enabling secure, scalable, and high-performance system-to-system communication.
• Experience developing modern web applications and user interfaces using React, Angular, JavaScript, TypeScript, AJAX, HTML5, CSS3, and Streamlit.
• Built responsive dashboards, reporting applications, and data visualization solutions with seamless backend API integrations.
• Developed reusable UI components and modern frontend design practices to improve usability and performance.
• Developed ETL workflows and data pipelines for analytics, reporting, model training, and business intelligence.
• Analyzed financial and business data using Python and SQL, collaborating with cross-functional stakeholders and improving model performance and governance.

AI and Machine Learning

• AI/ML and Generative AI engineering experience delivering GenAI powered assistants, intelligent automation, AI-driven workflows, and decision-support solutions.
• Developed enterprise GenAI solutions using LangChain, LangGraph, AI agents, RAG frameworks, vector databases, embedding models, and MCP integrations.
• Applied prompt engineering and LLM optimization techniques for financial reporting, document summarization, intelligent search, client communications, and workflow automation.
• Built and orchestrated multi-agent workflows, tool-calling frameworks, and retrieval pipelines to improve AI reasoning and response accuracy.
• Authored model documentation, prompt strategies, and governance artifacts to support audit and compliance needs.

DevOps and CI/CD

• DevOps engineering experience implementing CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, and AWS DevOps services.
• Automated infrastructure provisioning and deployments using Terraform, CloudFormation, Docker, Kubernetes (EKS), and IaC practices.
• Integrated DevSecOps controls, automated testing, code quality validation, and security scanning to improve reliability and compliance.
• Implemented monitoring, logging, and observability with AWS CloudWatch and Splunk, supporting lifecycle management of applications and AI models.