Role Summary:

We are seeking a Senior AI Engineer with deep expertise in multi-agent AI systems (agentic architectures) to design and deliver next-generation intelligent solutions at enterprise scale. This role requires a strong foundation in traditional software engineering combined with advanced capabilities in LLMs, autonomous agents, orchestration frameworks, and distributed AI systems.

The ideal candidate has evolved from full-stack or backend engineering into AI/ML, bringing both engineering rigor and innovation to build robust, secure, and scalable AI-driven platforms in regulated environments such as banking and financial services.

Key Responsibilities:

1. Multi-Agent System Design & Development

• Design and implement multi-agent architectures for enterprise use cases (decisioning systems, workflow automation, intelligent assistants).
• Develop cooperative, autonomous, and goal-driven agents with capabilities such as:
  • Planning and reasoning
  • Tool usage and API orchestration
  • Memory management (short-term + long-term)
• Build agent ecosystems leveraging frameworks such as:
  • LangChain / LangGraph
  • AutoGen / CrewAI
  • Semantic Kernel
  • Custom orchestration frameworks

2. AI/ML Solution Engineering

• Develop and deploy LLM-powered solutions, including:
  • Retrieval-Augmented Generation (RAG)
  • Fine-tuning / prompt engineering / evaluation pipelines
• Build scalable pipelines for:
  • Data ingestion and embedding
  • Vector search and knowledge retrieval
• Integrate AI with enterprise systems (trade platforms, risk engines, reporting systems)

3. Enterprise-Grade Architecture

• Design solutions aligned with enterprise non-functional requirements:
  • Security, auditability, explainability
  • Scalability and high availability
  • Cost optimization of LLM usage
• Implement observability for AI systems:
  • Prompt tracing, agent behavior monitoring, hallucination detection
• Ensure compliance with regulatory standards (e.g., RBI, data localization, risk controls)

4. Engineering Excellence

• Apply best practices in:
  • CI/CD pipelines (e.g., GitHub Actions, Harness)
  • Containerization (Docker, ECS/Kubernetes)
  • Infrastructure as Code (Terraform/CDK)
• Implement robust testing strategies:
  • Prompt testing
  • Agent behavior validation
  • Model performance benchmarking

5. Collaboration & Leadership

• Partner with business, product, and risk stakeholders to identify high-value AI use cases.
• Mentor junior engineers and drive AI capability uplift across teams.
• Lead architecture reviews and design governance for AI-based platforms.

Required Qualifications:

Education

• Bachelor’s or Master’s degree in Computer Science, Engineering, AI/ML, or related field

Experience

• Min 10+ years of software engineering experience with at least 3–5 years in AI/ML
• Proven experience in building production-grade AI systems in large enterprises
• Demonstrated progression from traditional engineering (Java/Python/Distributed Systems) to AI/ML

Technical Skills:

Core AI / Agentic Systems

• Strong understanding of:
  • Multi-agent architectures
  • Autonomous systems & reasoning models
  • Prompt engineering and LLM lifecycle
• Experience with:
  • OpenAI / Azure OpenAI / Anthropic / open-source LLMs
  • Agent frameworks (LangChain, AutoGen, CrewAI, Semantic Kernel)

Data & ML Engineering

• RAG architectures, vector databases
• Model evaluation frameworks and prompt optimization
• Python ecosystem (PyTorch, TensorFlow, Hugging Face)

Software Engineering

• Strong background in:
  • Distributed systems
  • Microservices architecture
• Languages: Python (primary), Java/Scala (preferred)
• API design and integration patterns

Cloud & DevOps

• Hands-on with AWS / Azure (preferred: AWS AI stack)
• Experience with:
  • Containers (Docker, Kubernetes/ECS)
  • CI/CD tools (Harness, GitHub Actions)
  • Monitoring tools (Prometheus, Grafana, OpenTelemetry)

Preferred Qualifications

• Experience in banking / financial services (FX trading, risk, compliance, post-trade processing)
• Familiarity with:
  • Regulatory frameworks (RBI, Basel, data governance)
  • Secure AI design and model risk management
• Experience building AI copilots or enterprise assistants
• Exposure to graph-based workflows and knowledge graphs

Key Competencies

• Architectural Thinking: Ability to design scalable, composable AI systems
• Problem Solving: Strong analytical mindset for complex enterprise use cases
• Innovation: Ability to translate emerging AI trends into practical solutions
• Execution Focus: Deliver high-quality, production-ready systems
• Stakeholder Engagement: Communicate effectively with business and senior leadership

Success Metrics

• Successful deployment of multi-agent AI solutions in production
• Measurable improvements in:
  • Process automation efficiency
  • Decision quality and latency
  • Developer productivity (via AI-assisted workflows)
• Adoption of AI capabilities across enterprise platforms

About State Street

Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.

We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.

As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.

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