Senior Lead Software Engineer- Python AI

JPMorgan Chase & Co.•Plano, TX

2d•$175,750 - $260,000

About The Position

The Senior Lead Software Engineer will enhance products across the product line by identifying, prioritizing, and delivering AI-driven features that improve user experience, efficiency, and operational resilience. The role designs, builds, and deploys production-grade AI applications, including LLM-powered workflows, Retrieval-Augmented Generation (RAG) solutions, and agentic systems. It also develops enterprise-ready agentic workflows, automation solutions, and self-healing patterns to reduce manual effort and recurring operational issues. The position architects AI systems with attention to latency, scalability, cost, model selection, observability, reliability, and failure modes. It establishes AI engineering standards, drives architecture reviews, contributes to reference architectures, and identifies and mitigates AI lifecycle risks such as privacy, security, bias, model misuse, prompt injection, and vendor risk. The engineer partners with product, design, risk, legal/compliance, and engineering stakeholders; evaluates vendors and platforms; researches emerging AI capabilities; and enables teams through documentation, workshops, design reviews, and coaching. The role includes hands-on development, secure production coding, troubleshooting, code review, and end-to-end ownership of outcomes across teams.

Requirements

Formal training or certification in software engineering concepts and 5+ years of applied engineering experience.
Hands-on experience delivering AI-powered features in production or near-production environments, such as LLMs, RAG, agents, workflow automation, or decision support systems.
Strong proficiency in Python and ability to build maintainable services, libraries, and pipelines.
Experience building APIs and services using Django, FastAPI, or similar frameworks.
Experience building modern UIs using React or similar frameworks, or strong ability to partner closely with frontend teams.
Strong system design skills, particularly for secure distributed systems, reliability, and operational excellence.
Practical understanding of Responsible AI and model risk concepts, including evaluation, guardrails, privacy, bias, explainability where applicable, and safe deployment practices.
Experience implementing observability through logging, metrics, and tracing, and AI-specific monitoring for quality, drift signals, latency, cost, and error rates.
Experience with cloud platforms; AWS is preferred.
Experience with containerized deployments.
Strong problem-solving skills and a structured, evidence-based approach; comfortable operating in ambiguity and delivering iteratively.

Nice To Haves

Experience with AI orchestration and agent frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, or similar.
Experience with vector search and retrieval patterns, including pgvector, Pinecone, Weaviate, OpenSearch/Elastic, or similar.
Experience designing and operating RAG systems, including chunking strategies, retrieval evaluation, citation/attribution patterns, caching, and fallback behaviors.
Familiarity with AI governance and standards, including model documentation, risk assessments, control frameworks, and secure-by-design patterns, and applying them to real delivery timelines.
Strong Infrastructure-as-Code experience, such as Terraform, and CI/CD practice.
Experience with data engineering ecosystems, such as Databricks, streaming, data quality controls, and lineage concepts.

Responsibilities

Enhance products across the product line by identifying, prioritizing, and delivering AI-driven features that improve user experience, efficiency, and operational resilience.
Design, build, and deploy production-grade AI applications, including LLM-powered workflows, RAG solutions, and agentic systems.
Build agentic workflows with enterprise-ready controls for identity, authorization, guardrails, tracing, evaluation, and auditability.
Develop automation solutions that reduce manual effort and recurring operational issues, including self-healing patterns and intelligent triage.
Architect AI systems with production concerns in mind, including latency, scalability, cost management, model selection, observability, reliability, and failure modes.
Establish and apply AI engineering standards across the product line, including prompting, RAG design, evaluation, logging and telemetry, and release readiness.
Drive AI architecture reviews and contribute to reference architectures for model hosting, orchestration, data access, and secure tool execution.
Identify and mitigate AI lifecycle risks, including data privacy, security, bias, model misuse, prompt injection, and third-party/vendor risk; ensure appropriate documentation and controls.
Partner with product, design, risk, legal/compliance, and engineering stakeholders to align on outcomes, constraints, and measurable success metrics.
Lead technical evaluations of vendors and platform providers, assessing capabilities, integration fit, security posture, and total cost of ownership.
Research emerging AI capabilities and translate findings into practical guidance, prototypes, and adoption recommendations.
Educate and enable teams through documentation, workshops, design reviews, and coaching.
Develop and troubleshoot software, write secure, high-quality production code, and review and debug code written by others.
Influence stakeholders and drive alignment across teams without direct authority, owning outcomes end-to-end.

Benefits

Competitive total rewards package, with base salary determined by role, experience, skill set, and location.
Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
Comprehensive health care coverage.
On-site health and wellness centers.
Retirement savings plan.
Backup childcare.
Tuition reimbursement.
Mental health support.
Financial coaching.

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