AI Engineer- remote

Mission

Design, build and deploy enterprise-grade AI solutions, autonomous AI agents, and multi-agent orchestration frameworks that support digital transformation initiatives, with a focus on financial services, anti-financial crime processes, and scalable AI platforms. Contribute to the development of secure, reliable, and production-ready AI capabilities that enable organizations to leverage Generative AI and LLM technologies at scale.

Responsibilities:

• Architect and develop production-grade autonomous agents and multi-agent orchestration frameworks (e.g., LangChain, AutoGen, CrewAI).
• Design, build, and deploy AI solutions based on Large Language Models (LLMs) and agentic architectures.
• Guide technical implementations across multiple parallel squads, ensuring consistent and reusable architectural standards.
• Integrate LLMs with external APIs, proprietary tools, databases, and enterprise systems to enable advanced tool-calling capabilities.
• Optimize prompt engineering approaches, context management, state management, and long-running agent workflows.
• Implement monitoring, logging, evaluation, and observability frameworks for AI applications and autonomous agents.
• Apply MLOps/AIOps practices, including model versioning, testing, performance evaluation, and monitoring.
• Design and deploy AI solutions on cloud platforms, primarily AWS, while collaborating on Azure and Databricks environments where applicable.
• Collaborate with business, data science, engineering, and cloud teams to deliver AI-powered solutions in regulated environments.

Professional Experience

• 5-10 years of professional experience in AI Engineering, Software Engineering, Machine Learning, or related fields.
• Experience designing and implementing enterprise AI solutions.
• Experience building and deploying autonomous AI agents and multi-agent systems.
• Experience integrating Generative AI and LLM technologies into enterprise applications.

Technical Skills

• Strong Python development skills.
• Hands-on experience with AWS, including AWS Bedrock and AgentCore.
• Experience with AI/LLM integration patterns such as:
  • MCP (Model Context Protocol)
  • A2A (Agent-to-Agent communication)
  • Structured Outputs
  • Skills-based architectures
• Knowledge of agent orchestration frameworks such as LangChain, AutoGen, CrewAI, or similar.
• Experience with MLOps/AIOps practices:
  • Versioning
  • Testing
  • Monitoring
  • Evaluation frameworks
• Experience integrating AI solutions with APIs, databases, and enterprise platforms.

Nice to Have

• Experience with Azure AI services.
• Experience with Databricks.
• Experience in Financial Services, Insurance, AML, KYC, Fraud Prevention, or other regulated industries.