Assistant Vice President - Data Science

Citi · Bengaluru · 10+ yrs experience · Posted 2026-07-18

Tech stack: Kubernetes, Python

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About the role

C12: Lead AI Engineer Job Title: Lead AI Engineer Job Code: TBD
Responsibilities: - The Lead AI Engineer is a senior individual contributor and the primary technical owner for complex AI projects.
- This role focuses on the hands-on architecture and implementation of cutting-edge AI solutions, ensuring technical excellence and alignment with product goals.
- Primary Responsibilities (60%):
- Serve as the technical anchor for the development of enterprise-grade, AI-powered solutions, guiding the implementation from a technical perspective.
- Lead the hands-on implementation of novel AI solutions, particularly in autonomous agents and advanced agentic architectures (e.g., using Google ADK).
- Architect and implement advanced frameworks for AgentOps and agentic AI governance, including evaluation suites, post-production observability, and traceability mechanisms.
- Secondary Responsibilities (30%):
- Partner with product leadership to shape the AI product strategy and technical roadmap.
- Technically lead the reimagination of core business processes by designing and implementing novel AI-driven solutions.
- Collaborate cross-functionally with Technology, Model Risk Management (MRM), Legal, Compliance, and Business teams to ensure solutions are robust, compliant, and aligned with enterprise goals.
- Additional Responsibilities (10%):
- Evangelize AI best practices across the organization.
- Lead the evaluation and integration of emerging AI technologies.
- Leadership & Collaboration / Dual-Track Path:
- Technical Leadership (Individual Contributor Track):
- Focus on solving the most challenging technical problems, pioneering new AI capabilities, and acting as a subject matter expert.
- People Leadership (Manager Track):
- Guide and grow a team of AI engineers, balancing hands-on technical contribution with coaching, performance management, and strategic project oversight.
Qualifications: - 8–10 years of professional experience in software engineering, with a significant focus on building and deploying large-scale AI/ML systems.
- and Skills (Required):
- Expertise in Python.
- Proven experience architecting and building complex systems using agentic frameworks (e.g., Google ADK, LangChain, AutoGen).
- Deep expertise in context optimization, knowledge storage (vector databases, knowledge graphs), and Retrieval-Augmented Generation (RAG) at scale.
- Strong architectural skills in designing complex, distributed systems and scalable backend APIs.
- Expertise in defining and implementing evaluation strategies using platforms like LangFuse.
- and Skills (Preferred): building control and sandboxing systems for AI research.
- Contributions to open-source AI or cloud-native projects.
- in the financial services industry.
- Deep hands-on knowledge of Kubernetes.
- Extensive experience with deep learning frameworks and MLOps principles.
- Certifications: Advanced certifications in Gen AI, Agentic AI, cloud architecture, Kubernetes, or machine learning are a strong plus.
- Bachelor's/University degree in Computer Science or a related field; Master's degree is highly preferred.
- 609912

Qualifications

- 8–10 years of professional experience in software engineering, with a significant focus on building and deploying large-scale AI/ML systems.
- and Skills (Required):
- Expertise in Python.
- Proven experience architecting and building complex systems using agentic frameworks (e.g., Google ADK, LangChain, AutoGen).
- Deep expertise in context optimization, knowledge storage (vector databases, knowledge graphs), and Retrieval-Augmented Generation (RAG) at scale.
- Strong architectural skills in designing complex, distributed systems and scalable backend APIs.
- Expertise in defining and implementing evaluation strategies using platforms like LangFuse.
- and Skills (Preferred): building control and sandboxing systems for AI research.
- Contributions to open-source AI or cloud-native projects.
- in the financial services industry.
- Deep hands-on knowledge of Kubernetes.
- Extensive experience with deep learning frameworks and MLOps principles.
- Certifications: Advanced certifications in Gen AI, Agentic AI, cloud architecture, Kubernetes, or machine learning are a strong plus.
- Bachelor's/University degree in Computer Science or a related field; Master's degree is highly preferred.
- 609912

Responsibilities

- The Lead AI Engineer is a senior individual contributor and the primary technical owner for complex AI projects.
- This role focuses on the hands-on architecture and implementation of cutting-edge AI solutions, ensuring technical excellence and alignment with product goals.
- Primary Responsibilities (60%):
- Serve as the technical anchor for the development of enterprise-grade, AI-powered solutions, guiding the implementation from a technical perspective.
- Lead the hands-on implementation of novel AI solutions, particularly in autonomous agents and advanced agentic architectures (e.g., using Google ADK).
- Architect and implement advanced frameworks for AgentOps and agentic AI governance, including evaluation suites, post-production observability, and traceability mechanisms.
- Secondary Responsibilities (30%):
- Partner with product leadership to shape the AI product strategy and technical roadmap.
- Technically lead the reimagination of core business processes by designing and implementing novel AI-driven solutions.
- Collaborate cross-functionally with Technology, Model Risk Management (MRM), Legal, Compliance, and Business teams to ensure solutions are robust, compliant, and aligned with enterprise goals.
- Additional Responsibilities (10%):
- Evangelize AI best practices across the organization.
- Lead the evaluation and integration of emerging AI technologies.
- Leadership & Collaboration / Dual-Track Path:
- Technical Leadership (Individual Contributor Track):
- Focus on solving the most challenging technical problems, pioneering new AI capabilities, and acting as a subject matter expert.
- People Leadership (Manager Track):
- Guide and grow a team of AI engineers, balancing hands-on technical contribution with coaching, performance management, and strategic project oversight.

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