DTCC

Clears and settles securities trades for financial firms

AI & Data Engineering Architect

AI Application EngineeringConfirmed live in the last 24 hours

Full-timeHyderabad, Telangana, India

About the Job

Requirements

Deep expertise in Agentic AI architectures, multi-agent collaboration frameworks, and autonomous workflow orchestration.
Strong experience building enterprise-grade Retrieval-Augmented Generation (RAG) systems, semantic search solutions, vector databases, and knowledge retrieval platforms.
Advanced proficiency in Python, AI orchestration frameworks such as LangGraph, AutoGen, and LlamaIndex, and cloud AI platforms.
Extensive experience with Snowflake, Apache Iceberg, distributed data engineering, and large-scale enterprise data ecosystems.
Expertise in MLOps, continuous integration and continuous delivery (CI/CD), MLflow, Kubernetes, Docker, and production AI deployment patterns.
Strong understanding of AI governance, model security, compliance, privacy, and responsible AI principles.
Experience designing highly scalable, resilient, and secure AI platforms capable of supporting enterprise-wide adoption.

Responsibilities

Lead the architecture, design, and implementation of enterprise-scale Agentic AI platforms and intelligent automation solutions that support critical business functions and data-driven decision making.
Define and drive the organization's AI/ML architecture strategy, reference architectures, standards, and best practices across data, model, infrastructure, and governance layers.
Design and implement advanced multi-agent systems using frameworks such as LangGraph, AutoGen, LlamaIndex, and emerging agent orchestration technologies.
Architect complex Retrieval-Augmented Generation (RAG) solutions leveraging vector databases, knowledge graphs, semantic search, and enterprise data platforms.
Develop scalable agentic workflows incorporating tool-calling, memory management, planning, reasoning, reflection, and human-in-the-loop capabilities.
Lead the integration of LLM-powered applications utilizing Azure OpenAI, AWS Bedrock, and other enterprise AI services while ensuring performance, security, and cost optimization.
Design enterprise-grade data architectures utilizing Snowflake, Apache Iceberg, vector databases, and cloud-native data platforms to support AI and analytics workloads.
Establish standards and frameworks for model lifecycle management, experimentation, deployment, monitoring, observability, and governance using MLflow and modern MLOps practices.
Define and implement enterprise AI security controls, including OAuth authentication, fine-grained authorization, role-based access control, data protection, and model governance frameworks.
Drive AI platform modernization initiatives through containerization and orchestration technologies including Docker, Kubernetes, and cloud-native deployment architectures.
Establish reusable patterns for Model Context Protocol (MCP) integration, external tool orchestration, AI application interoperability, and agent communication frameworks.
Collaborate with business leaders, product owners, and engineering teams to identify AI transformation opportunities and translate business objectives into scalable technical solutions.
Lead architectural reviews and provide technical oversight for AI/ML projects across multiple teams and business domains.
Develop enterprise standards for RAG evaluation, prompt management, agent testing, model monitoring, AI safety, and responsible AI implementation.
Partner with Data Engineering teams to architect robust data ingestion, transformation, and orchestration pipelines using dbt, Airflow, Snowflake, and Apache Iceberg.
Drive continuous innovation by evaluating emerging AI technologies, open-source frameworks, foundation models, and industry best practices.
Serve as the organization's subject matter expert for Agentic AI, Generative AI, Enterprise AI Architecture, and Intelligent Automation.
Mentor and guide senior engineers, data scientists, AI developers, and solution architects on architecture decisions and engineering best practices.
Influence technology roadmaps and long-term platform strategy for AI, data, and analytics capabilities.
Establish AI Center of Excellence standards, reusable frameworks, and governance processes across the enterprise.
Lead proof-of-concepts, architecture assessments, technology evaluations, and vendor engagements.
Provide thought leadership on emerging AI trends, autonomous agents, foundation models, AI governance, and enterprise adoption strategies.

Are you ready to make an impact at DTCC?

Do you want to work on innovative projects, collaborate with a dynamic and supportive team, and receive investment in your professional development? At DTCC, we are at the forefront of innovation in the financial markets. We are committed to helping our employees grow and succeed. We believe that you have the skills and drive to make a real impact. We foster a thriving internal community and are committed to creating a workplace that looks like the world that we serve.

Pay and Benefits:

Competitive compensation, including base pay and annual incentive
Comprehensive health and life insurance and well-being benefits, based on location
Pension / Retirement benefits
Paid Time Off and Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being.
DTCC offers a flexible/hybrid model of 3 days onsite and 2 days remote (onsite Tuesdays, Wednesdays and a third day unique to each team or employee).

The Impact you will have in this role:

The Development family is responsible for creating, designing, deploying, and supporting applications, programs, and software solutions. May include research, new development, prototyping, modification, reuse, re-engineering, maintenance, or any other activities related to software products used internally or externally on product platforms supported by the firm. The software development process requires in-depth subject matter expertise in existing and emerging development methodologies, tools, and programming languages. Software Developers work closely with business partners and / or external clients in defining requirements and implementing solutions. The Software Engineering role specializes in planning, documenting technical requirements, designing, developing, and testing all software systems and applications for the firm. Works closely with architects, product managers, project management, and end-users in the development and enhancement of existing software systems and applications, proposing and recommending solutions that solve complex business problems.

Key Responsibilities

Lead the architecture, design, and implementation of enterprise-scale Agentic AI platforms and intelligent automation solutions that support critical business functions and data-driven decision making.
Define and drive the organization's AI/ML architecture strategy, reference architectures, standards, and best practices across data, model, infrastructure, and governance layers.
Design and implement advanced multi-agent systems using frameworks such as LangGraph, AutoGen, LlamaIndex, and emerging agent orchestration technologies.
Architect complex Retrieval-Augmented Generation (RAG) solutions leveraging vector databases, knowledge graphs, semantic search, and enterprise data platforms.
Develop scalable agentic workflows incorporating tool-calling, memory management, planning, reasoning, reflection, and human-in-the-loop capabilities.
Lead the integration of LLM-powered applications utilizing Azure OpenAI, AWS Bedrock, and other enterprise AI services while ensuring performance, security, and cost optimization.
Design enterprise-grade data architectures utilizing Snowflake, Apache Iceberg, Vector Databases, and cloud-native data platforms to support AI and analytics workloads.
Establish standards and frameworks for model lifecycle management, experimentation, deployment, monitoring, observability, and governance using MLflow and modern MLOps practices.
Define and implement enterprise AI security controls, including OAuth authentication, fine-grained authorization, role-based access control, data protection, and model governance frameworks.
Drive AI platform modernization initiatives through containerization and orchestration technologies including Docker, Kubernetes, and cloud-native deployment architectures.
Establish reusable patterns for Model Context Protocol (MCP) integration, external tool orchestration, AI application interoperability, and agent communication frameworks.
Collaborate with business leaders, product owners, and engineering teams to identify AI transformation opportunities and translate business objectives into scalable technical solutions.
Lead architectural reviews and provide technical oversight for AI/ML projects across multiple teams and business domains.
Develop enterprise standards for RAG evaluation, prompt management, agent testing, model monitoring, AI safety, and responsible AI implementation.
Partner with Data Engineering teams to architect robust data ingestion, transformation, and orchestration pipelines using dbt, Airflow, Snowflake, and Apache Iceberg.
Drive continuous innovation by evaluating emerging AI technologies, open-source frameworks, foundation models, and industry best practices.

Leadership & Strategic Responsibilities

Serve as the organization's subject matter expert for Agentic AI, Generative AI, Enterprise AI Architecture, and Intelligent Automation.
Mentor and guide senior engineers, data scientists, AI developers, and solution architects on architecture decisions and engineering best practices.
Influence technology roadmaps and long-term platform strategy for AI, data, and analytics capabilities.
Establish AI Center of Excellence standards, reusable frameworks, and governance processes across the enterprise.
Lead proof-of-concepts, architecture assessments, technology evaluations, and vendor engagements.
Provide thought leadership on emerging AI trends, autonomous agents, foundation models, AI governance, and enterprise adoption strategies.

Technical Expertise Expected

Deep expertise in Agentic AI architectures, multi-agent collaboration frameworks, and autonomous workflow orchestration.
Strong experience building enterprise-grade RAG systems, semantic search solutions, vector databases, and knowledge retrieval platforms.
Advanced proficiency in Python, AI orchestration frameworks (LangGraph, AutoGen, LlamaIndex), and cloud AI platforms.
Extensive experience with Snowflake, Apache Iceberg, distributed data engineering, and large-scale enterprise data ecosystems.
Expertise in MLOps, CI/CD, MLflow, Kubernetes, Docker, and production AI deployment patterns.
Strong understanding of AI governance, model security, compliance, privacy, and responsible AI principles.
Experience designing highly scalable, resilient, and secure AI platforms capable of supporting enterprise-wide adoption

Actual salary is determined based on the role, location, individual experience, skills, and other considerations. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

About the Company

DTCC

Clears and settles securities trades for financial firms

DTCC provides the systems financial firms use to complete securities trades after buyers and sellers agree a transaction. Broker-dealers, banks and asset managers use its services to match trade details, calculate obligations, move securities and report transactions. Its NSCC and FICC subsidiaries clear equity and fixed-income trades, while DTC holds securities and handles settlement and payments such as dividends. By offsetting obligations between firms, its clearing services reduce the securities and cash that must change hands, and its repositories maintain derivatives transaction records for regulatory reporting. The parent was established in 1999 and is headquartered in Jersey City, New Jersey.

Company size: 5,001-10,000
Company stage: Not listed
Total funding: Not listed
Headquarters: Jersey City, NJ
Founded: 1999

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