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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Lead Software Engineer- Python AI - **Company:** JPMorgan Chase & Co. - **Location:** Columbus, OH, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Continuous Integration, Information Engineering, Data Security, Software Debugging, Decision Support Systems, Software Design Patterns, Distributed Systems, Django Web Framework, Design of User Interfaces, Human-Computer Interaction, Python (Programming Language), Search Technologies, Software Engineering, Software Systems, Data Streaming, Data Logging, Pinecone, Scripting, LangChain, ReactJS, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, IT Architecture, Model Validation, Llamaindex, Generative AI, Agentic-AI, Fastapi, Pgvector, Containerization, Low Latency, Production Code, CrewAI, Weaviate, Machine Learning Operations, Front End Software Development, Api Design, Terraform, Semantic Kernel, OpenSearch, Databricks - **Published:** November 7, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-lead-software-engineer-python-ai-columbus-oh--430e810f-42fc-498d-ba88-dc9f00f92d33 ## About the Role Software Engineering, Python Programming/Scripting Language, Artificial Intelligence (AI), Establish Priorities, Artificial Intelligence (AI), User Interface/Experience (UI/UX), Process Improvement, Operational Strategy, Automation, Cost Control, * 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 (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 with an emphasis on secure distributed systems, reliability, and operational excellence * Practical understanding of Responsible AI / model risk concepts (evaluation, guardrails, privacy, bias, explainability where applicable, and safe deployment practices) * Experience implementing observability (logging, metrics, tracing) and AI-specific monitoring (quality, drift signals, latency, cost, error rates) * Experience with cloud platforms (AWS preferred) and containerized deployments * Strong problem-solving skills with a structured, evidence-based approach; comfortable operating in ambiguity and delivering iteratively Preferred Qualifications, Capabilities, and Skills * Experience with AI orchestration and agent frameworks (e.g., 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 (chunking strategies, retrieval evaluation, citation/attribution patterns, caching, fallback behaviors) * Familiarity with AI governance and standards (model documentation, risk assessments, control frameworks, secure-by-design patterns) and applying them to real delivery timelines * Strong Infrastructure-as-Code experience (e.g., Terraform) and CI/CD practice, data engineering ecosystems (e.g., Databricks, streaming, data quality controls, lineage concepts) ## Description * Enhance multiple 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, Retrieval-Augmented Generation (RAG) solutions, and agentic systems * Build agentic workflows with enterprise-ready controls (identity, authorization, guardrails, tracing, evaluation, and auditability) * Develop automation solutions that reduce manual effort and eliminate recurring operational issues, including self-healing patterns and intelligent triage * Architect AI systems with production concerns in mind: latency, scalability, cost management, model selection, observability, reliability, and failure modes (including hallucinations and tool errors) * Establish and apply AI engineering standards across the product line (prompting patterns, RAG design standards, evaluation practices, logging/telemetry, and release readiness criteria) * Drive AI architecture reviews and contribute to reference architectures for model hosting, orchestration, data access, and secure tool execution * Identify and mitigate risks across the AI lifecycle including data privacy, security, bias, model misuse, prompt injection, and third-party/vendor risk, and 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 with vendors and platform providers; assess capabilities, integration fit, security posture, and total cost of ownership * Research emerging AI capabilities (models, frameworks, tools) and translate findings into practical guidance, prototypes, and adoption recommendations * Educate and enable teams through documentation, workshops, design reviews, and coaching to raise AI fluency and delivery consistency across the organization * Execute creative software solutions, technical troubleshooting, and hands-on development; write secure, high-quality production code and review/debug code written by others * Influence stakeholders and drive alignment across teams without direct authority, owning outcomes end-to-end