Job DescriptionThe Impact You Will Have in This RoleJoin our team to help deliver next-generation AI capabilities that transform how business processes are automated, optimized, and enhanced through Generative AI and Agentic AI solutions.
We are seeking a highly skilled
Senior AI Engineer with hands-on experience designing, building, integrating, and deploying enterprise-scale AI applications. This role focuses on leveraging, integrating, and orchestrating existing foundation models and AI services to solve real business problems through intelligent agents, agentic workflows, Retrieval-Augmented Generation (RAG), and enterprise integrations.
Unlike traditional AI research roles, this position emphasizes the integration, orchestration, and operationalization of existing AI capabilities rather than the development of foundational models.
You will partner closely with software engineers, architects, data engineers, and business stakeholders to take AI solutions from concept and prototype through production deployment. The ideal candidate combines strong Python engineering skills with practical experience developing AI-powered applications using Amazon Bedrock, AWS, Snowflake, and modern agent development frameworks.
Your Primary Responsibilities:
Build Enterprise AI Solutions - Design, develop, test, and deploy Generative AI and Agentic AI applications using Amazon Bedrock, AWS, and related AI technologies.
- Build intelligent AI agents and multi-agent systems that automate and enhance business workflows.
- Develop scalable AI-powered solutions that integrate seamlessly with enterprise applications and platforms.
Design Agentic Architectures - Design and implement agent orchestration patterns, including:
- Supervisor and coordinator agents
- Specialized task agents
- Routing and decisioning frameworks
- Context and memory management
- Workflow execution and monitoring
- Build multi-step reasoning workflows that effectively combine LLM intelligence with enterprise systems and business processes.
Develop AI Integrations - Integrate AI agents with:
- REST APIs
- Enterprise applications
- Internal platforms and services
- Databases and data platforms
- External tools and third-party systems
- Create secure and reusable tools, APIs, and integration layers that enable agent interaction with enterprise systems.
Implement Knowledge-Driven AI - Design and deliver Retrieval-Augmented Generation (RAG) solutions using enterprise documents, structured data, and knowledge repositories.
- Implement embeddings, vector search, document retrieval, and knowledge-grounding strategies to improve AI accuracy and business outcomes.
- Select and leverage appropriate foundation models based on business and technical requirements.
Build Production-Ready AI Applications - Develop backend AI services primarily using Python.
- Design prompts, agent instructions, tool definitions, workflows, and AI guardrails.
- Deploy and support AI applications within AWS and OpenShift (OCP) environments.
- Implement logging, monitoring, evaluation, security, observability, and operational controls for enterprise AI workloads.
Drive Engineering Excellence - Collaborate across engineering, architecture, product, and business teams to identify and deliver AI-enabled solutions.
- Troubleshoot and resolve issues involving models, agents, APIs, integrations, and downstream systems.
- Establish reusable engineering patterns, best practices, and governance standards for Agentic AI development.
- Provide technical leadership and mentorship to engineers and development teams.
Qualifications - 6-8 years of hands-on experience in Software Engineering, AI Engineering, Data Engineering, or related technical disciplines.
- Demonstrated success delivering production-grade AI, Machine Learning, or Generative AI solutions.
- Experience translating business use cases into scalable AI-enabled applications.
Talent Needed for SuccessAI & Software Engineering - Strong hands-on programming expertise in Python.
- Experience developing and deploying Generative AI and LLM-powered applications.
- Strong experience with Amazon Bedrock and AWS-based AI services.
- Experience designing and implementing AI agents and agentic workflows.
- Strong understanding of:
- Agent orchestration
- Multi-agent architectures
- Tool/function calling
- Agent memory
- Context management
- Prompt engineering
AI & Data Platforms - Hands-on experience with:
- Retrieval-Augmented Generation (RAG)
- Embeddings
- Vector databases and vector search
- Knowledge retrieval systems
- Enterprise document processing
- Strong Snowflake, SQL, and data integration experience.
Enterprise Integration - Experience integrating AI solutions with:
- REST APIs
- Enterprise applications
- Databases
- Internal and external services
- Experience deploying, supporting, and monitoring AI solutions in production environments.
Security & Governance - Strong understanding of:
- Authentication and authorization
- Enterprise security and data privacy
- Secrets management
- Responsible AI practices
- AI governance and guardrails
Professional Skills - Excellent analytical, problem-solving, and debugging skills.
- Strong verbal and written communication skills.
- Ability to effectively collaborate with engineering and business stakeholders.
- Demonstrated ability to work in fast-paced, highly collaborative environments.
Professional Skills - Experience with Agentic AI frameworks such as:
- CrewAI
- LangGraph
- AutoGen
- Similar orchestration frameworks
- Experience with Model Context Protocol (MCP).
- Experience building complex multi-agent collaboration systems.
- Experience with AI evaluation, monitoring, and observability frameworks.
- Experience using Kiro or other AI-assisted development tools.
- Experience with OpenShift (OCP), containers, and cloud-native deployments.
- Experience implementing CI/CD pipelines and DevOps practices for AI applications.
- Familiarity with Snowflake Cortex AI capabilities.
- Knowledge of enterprise AI governance, model monitoring, and responsible AI principles.
The salary range is indicative for roles at the same level within DTCC across all US locations. Actual salary is determined based on the role, location, individual experience, skills, and other considerations.
Learn more about Clearance and Settlement by clicking here.
About the TeamServes as a dedicated technology resource for advancing DTCC's business opportunities and providing industry thought leadership for leveraging new technology. The goal of this new department is to partner internally with IT, our business and regulatory divisions and externally with clients, regulators, and fintech vendors, to help build new platforms and business models to advance DTCC's mission to support the financial markets.
