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The Agentic AI Engineer is a strategic professional deeply immersed in the Artificial Intelligence and Machine Learning (AI/ML) field, with a strong, hands-on focus on leading agentic flow design, prompt design, and comprehensive testing for advanced AI systems. This role contributes to directional strategy by leveraging expertise in building autonomous and semi-autonomous AI solutions, particularly utilizing frameworks like Google Agent Development Kit (ADK), and applies this knowledge to critical business challenges.
Responsibilities (Key Areas):
Lead Agentic AI Flow Design, Prompt Engineering, and Testing: Accountable for leading the end-to-end design, development, and deployment of large-scale agentic AI solutions, with a primary focus on leveraging frameworks like Google Agent Development Kit (ADK). This includes taking ownership of agentic flow design, sophisticated prompt engineering, and comprehensive testing strategies to ensure the reliability, performance, and ethical behavior of autonomous AI systems. This role will define system function specifications through end-user consultation, integrate sophisticated AI/ML methodologies and MLOps practices, and ensure that agentic architectures meet business objectives and scale efficiently.
Drive AI Architecture and Best Practices for Agentic Systems: Provide critical architectural guidance for next-generation AI (NGAI) initiatives, ensuring adherence to best practices, CTO guidelines, and platform standards, particularly for agentic AI development. This involves actively participating in design review boards, publishing agentic AI design patterns, conducting rigorous code reviews for NGAI projects, and fostering a culture of technical excellence in AI engineering. Strategic Planning and Risk Management for Agentic AI Initiatives: Assist in the strategic planning and management of complex AI/ML development assignments, including cross-functional projects and those with significant budgets, with a focus on agentic AI. This entails meticulously identifying, analyzing, and communicating technical and business risks specific to AI, leading smart trade-off discussions with stakeholders, and developing clear delivery plans that navigate dependencies and maximize resources for the Digital organization, especially within the context of agentic systems. Mentorship, Continuous Improvement, and Innovation in Agentic AI: Empower and mentor AI/ML Engineers (SDEs) on best practices in agentic AI development, Google Agent Development Kit (ADK), and other advanced AI technologies. Drive continuous improvement in AI processes, advocate for the adoption of innovative AI technologies, and demonstrate leadership in influencing scrum teams beyond direct reporting lines to advance the firm's capabilities in autonomous AI, including prompt and flow optimization.
Qualifications:
6-10 years of relevant experience in an AI/ML development role or senior-level experience in an AI role
Demonstrable and significant experience as a lead developer for agentic flow design, prompt design, and comprehensive testing of autonomous or semi-autonomous AI systems, with deep expertise in Google Agent Development Kit (ADK).
Lead resources and serve as a functional SME across the company through advanced knowledge of AI algorithms, data structures, distributed AI systems, MLOps, and the use of specific AI frameworks/libraries to lead, architect, and drive broader adoption forward.
Acquire relevant AI technology and financial industry skills (e.g., cloud AI services, MLOps, agentic platforms) and understand all aspects of Next Gen AI (NGAI) technology – including innovative approaches and new opportunities.
Demonstrate knowledge on automating AI model quality, model performance, unit testing for AI components, and MLOps build processing in the CI/CD pipeline, including considerations for agentic systems.
Required Skills:
Programming Languages: Strong proficiency in Python is paramount, followed by R, Java, or C++ for scalable, high-performance applications.
Prompt Engineering & Generative AI: Ability to design, test, and refine prompts for large language models (LLMs) to ensure reliable outputs, with specific experience in constructing complex agentic prompts and flows.
Machine Learning & Deep Learning: Expertise in building models, understanding algorithms (supervised/unsupervised), and neural network architectures (CNNs, RNNs, Transformers).
AI Frameworks & Libraries: Proficiency with tools like TensorFlow, PyTorch, Keras, Scikit-learn.
Data Engineering: Skills in data cleaning, preprocessing, visualization, and working with databases (SQL/NoSQL) and data lakes.
Cloud Computing & DevOps: Experience with cloud platforms (AWS, Azure, Google Cloud) and deploying models using MLOps practices for automation.
Mathematics & Statistics: Strong understanding of linear algebra, calculus, and probability for developing and refining algorithms.
Soft Skills: Strong problem-solving, analytical thinking, and collaboration skills are essential to bridge the gap between technical models and business needs.
Key Tools & Technologies:
Version Control: Git/GitHub.
IDEs/Notebooks: VS Code, PyCharm, Jupyter.
Frameworks:Google Agent Development Kit (ADK).
Education:
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Technology
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Digital Software Engineering
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Full time
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Tampa Florida United States
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$125,600.00 - $188,400.00
In addition to salary, Citi’s offerings may also include, for eligible employees, discretionary and formulaic incentive and retention awards. Citi offers competitive employee benefits, including: medical, dental & vision coverage; 401(k); life, accident, and disability insurance; and wellness programs. Citi also offers paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays. For additional information regarding Citi employee benefits, please visit citibenefits.com. Available offerings may vary by jurisdiction, job level, and date of hire.
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Please see the requirements listed above.
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For complementary skills, please see above and/or contact the recruiter.
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