The Digital Software Engineering Lead Analyst is a strategic technical leader responsible for designing and engineering enterprise grade Agentic AI solutions capable of integrating data from multiple heterogeneous systems and operating reliably at scale.
You will act as a hands-on architect, engineer, and partner to cross functional teams—including Data Engineering, Architecture, Enterprise Platforms, and Product—defining the technical approach, AI system design, and integration patterns needed to build robustfault tolerantnt AI agents and AIdriven automation capabilities.
This role requires deep technical breadth across machine learning, LLMs, data pipelines, cloud engineering, orchestration, and modern AI frameworks. The solutions you design will enable strategic automation, cognitive decisioning, and dynamic multi-agent workflows across the organization.
Key Responsibilities
AI Solution Architecture & Agentic Systems
- Design and build agentic AI systems, including autonomous agents, multiagent orchestration, tool use, and adaptive decision-making workflows.
- Architect fault tolerant, scalable AI solutions using modern agent frameworks (e.g., Google_ADK, LangGraph, LangChain , OpenAI Assistants, CrewAI, AutoGen, custom orchestrators).
- Define the end-to-end AI system blueprint, including knowledge integration, orchestration, pipelines, observability, governance, and failover strategies.
- Evaluate and select LLMs, embeddings, vector stores, and middleware best suited for complex enterprise requirements.
Data Integration & Pipeline Engineering
- Partner with engineering teams to aggregate, ingest, and harmonize data from multiple systems, including APIs, databases, internal platforms, and unstructured sources.
- Design robust data pipelines optimized for LLM workloads (e.g., chunking, metadata design, semantic indexing, retrieval strategies).
- Implement mechanisms for ensuring data freshness, quality, and fault tolerance across distributed systems.
LLM, RAG, and Generative AI Engineering
- Build advanced Retrieval-Augmented Generation (RAG) architectures, including hybrid retrieval, query planning, and retrieval optimization.
- Develop, tune, and deploy applications leveraging major LLMs (OpenAI, Gemini, Claude, Llama, Mistral, HuggingFace ecosystem).
- Engineer prompts, system instructions, and reusable prompt templates for deterministic AI behavior.
- Implement safety guardrails, evaluation pipelines, and bias/error mitigation strategies.
AI Platform Engineering & Deployment
- Develop cloudnative GenAI applications using containerized infrastructure (Kubernetes, OpenShift, Docker).
- Build and support production-grade MLOps / AIOps pipelines, including CI/CD, automated testing, monitoring, model versioning, and rollback strategies.
- Partner with engineering teams to ensure secure, compliant deployment of all AI workloads.
Technical Leadership & Collaboration
- Serve as technical SME for AI engineering patterns, solution design, and architecture.
- Mentor mid-level engineers and analysts, guiding best practices in AI build patterns and engineering quality.
- Influence product and platform strategy by providing insights on emerging GenAI and agentic technologies.
Qualification:
Experience
- 10+ years of experience in software engineering, AI/ML engineering, systems architecture, or related fields.
- Proven experience designing and deploying enterprisegrade AI Systems in production.
Required Technical Skills
Core AI/ML & GenAI Expertise
- Strong foundations in ML, NLP, embeddings, statistics, neural networks, and LLMs.
- Extensive handson experience with LLMs: Gemini, OpenAI, Claude, Mistral, Llama, opensource models, etc.
- Deep expertise in RAG architectures, including retrieval optimization, vector search, and semantic data modeling.
- Experience building agentic AI using Google_ADK or langGraph
Programming & Data Engineering
- Strong proficiency in Python and libraries such as:
Pandas, NumPy, scikitlearn, PyTorch, TensorFlow, Transformers, FastAPI, LangChain, LlamaIndex.
- Hands-on experience with vector databases: Pinecone, PGVector, MongoDB Atlas Vector Search, Neo4j, Milvus, etc.
- Experience building pipelines for large-scale unstructured data processing.
Cloud, DevOps, & MLOps
- Strong CI/CD experience: GitLab CI, Jenkins, Azure DevOps, ArgoCD, GitHub Actions.
- Expertise deploying GenAI solutions in production using:
Kubernetes, Docker, Helm, serverless runtimes, or cloud-native LLM services.
- Experience with monitoring, observability, and logging frameworks relevant for AI workloads.
Soft Skills
- Exceptional problem-solving and analytical skills.
- Ability to execute independently while operating effectively in ambiguity.
- Strong collaboration skills across engineering, architecture, and product teams.
- Deep commitment to ethics, transparency, and responsible AI usage.
Preferred Qualifications
- Experience building AI systems in regulated or enterprise environments.
- Experience using knowledge graphs, graph databases, or enterprise metadata systems.
- Familiarity with AIOps, agent monitoring, or AI governance frameworks.
Education
- Bachelor’s degree or equivalent experience required.
- Master’s degree preferred.
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Job Family Group: Technology------------------------------------------------------
Job Family:Applications Development------------------------------------------------------
Time Type:Full time------------------------------------------------------
Primary Location:Tampa Florida United States------------------------------------------------------
Primary Location Full Time Salary Range:$141,440.00 - $212,160.00
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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Most Relevant Skills Please see the requirements listed above.------------------------------------------------------
Other Relevant Skills For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
Anticipated Posting Close Date:Nov 06, 2026------------------------------------------------------
Automated Processing and AIWe use automated processing, including artificial intelligence, for our legitimate business interests (or our reasonable and appropriate business purposes) to identify and align the candidate's skills and abilities with a specific job opening. Additionally, if you so choose, or consent, we can match your skills and abilities to other suitable roles at Citi.
Importantly, all our hiring processes and decisions, including determining your suitability for a role, are conducted, checked, and decided by individuals. Our automated processing and AI do not involve relying on automatic or autonomous decision-making. Please refer to any Jurisdictional Considerations, with specific provisions for your country (where relevant) for further details.
Illinois residents – AI Notice and Right
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Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.
If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi.
View Citi’s EEO Policy Statement and the Know Your Rights poster.
Skills Required
- 10+ years of experience in software engineering, AI/ML engineering, systems architecture, or related fields
- Experience designing and deploying enterprise-grade AI systems in production
- Strong foundations in machine learning, NLP, embeddings, statistics, neural networks, and LLMs
- Hands-on experience with LLMs including Gemini, OpenAI, Claude, Mistral, Llama, or open-source models
- Deep expertise in RAG architectures, retrieval optimization, vector search, and semantic data modeling
- Experience building agentic AI using Google ADK or LangGraph
- Strong proficiency in Python and listed machine learning and AI libraries
- Hands-on experience with vector databases such as Pinecone, PGVector, MongoDB Atlas Vector Search, Neo4j, or Milvus
- Experience building pipelines for large-scale unstructured data processing
- Strong CI/CD experience with GitLab CI, Jenkins, Azure DevOps, ArgoCD, or GitHub Actions
- Expertise deploying GenAI solutions using Kubernetes, Docker, Helm, serverless runtimes, or cloud-native LLM services
- Experience with monitoring, observability, and logging frameworks for AI workloads
- Exceptional problem-solving and analytical skills
- Ability to execute independently in ambiguous environments
- Strong collaboration skills across engineering, architecture, and product teams
- Commitment to ethics, transparency, and responsible AI usage
- Bachelor's degree or equivalent experience
- Experience building AI systems in regulated or enterprise environments
- Experience using knowledge graphs, graph databases, or enterprise metadata systems
- Familiarity with AIOps, agent monitoring, or AI governance frameworks
- Master's degree
What We Do
Citigroup is a global diversified financial services holding company serving consumers, corporations, governments and institutions. It provides consumer banking and credit, corporate and investment banking, securities brokerage, trade and securities services, and wealth management. Operating across nearly 160 countries and jurisdictions, Citi aims to be a leading partner for institutions with cross-border needs, a global wealth manager, and a valued personal bank in the United States.
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