Technology Support Engineer - Java, Python, AWS, and Large Language Models

Technology Support I - Java/Python, UI, AWS, LLM

Embark on a dynamic career in tech support, where your skills contribute to maintaining world-class technology solutions to ensure a seamless user experience.

As a Technology Support I team member in Commercial & Investment Bank’s Markets Tech team, you will ensure the operational stability, availability, and performance of our production application flows. Be part of the team responsible for troubleshooting, maintaining, identifying, escalating, and resolving production service interruptions for all internally and externally developed systems, ensuring a seamless user experience.

 

Job responsibilities

• Execute creative LLM assisted software solutions, design, develop, and troubleshoot LLM‑powered applications and services (e.g., retrieval‑augmented generation, agent workflows, structured extraction, classification) with a willingness to think beyond routine approaches to break down technical problems and deliver measurable outcomes and think in the novel Agentic AI way.
• Develop data quality rules and controls using LLM, define and enforce guardrails for prompts, retrieved context, model inputs/outputs, and post‑processing, including PII redaction, toxicity/safety filters, hallucination mitigation, output schema validation, and policy compliance.
• Provide Level 3 (L3) support for LLM assisted production systems, own complex incidents, model and prompt rollouts/rollbacks, dependency issues (vector stores, embeddings, feature stores), and ensure high availability, reliability, and adherence to SLAs including latency and cost budgets.
• Support BAU operations for Markets businesses: maintain and evolve LLM use cases supporting markets workflows with disciplined change management, canary releases, A/B tests, and close partnership with product, controls, and operations.
• Create secure, high‑quality production code: implement LLM assisted micro services, synchronous and asynchronous inference pipelines (streaming where appropriate), deterministic fallbacks, circuit breakers, and observability for reliability in production.
• Produce architecture and design artifacts, deliver model cards, system/data lineage, RAG/agent reference architectures, prompt libraries and versioning strategies, evaluation plans, and control evidence ensuring design constraints and regulatory expectations are met during development.
• Identify hidden problems and patterns, use telemetry, error analysis, prompt and context analytics, and drift detection to improve model selection, prompt strategies, retrieval quality, chunking/embedding strategies, and system architecture.
• Drive LLM Ops best practices, integrate models, prompts, and evaluation into CI/CD, enforce approvals, segregation of duties, and reproducibility, automate regression and guardrail tests and manage lifecycle across environments.
• Ensure that model strengths, limitations, and risk profiles are understood, documented, and appropriately applied across different classes of software work, and maintain deep understanding of the strengths, limitations, and risk characteristics of approved LLMs (e.g., Claude, Chat GPT, and successor models), including safety profiles, context limits, determinism strategies, and fine tuning vs. prompt only tradeoffs, design multi agent workflows that incorporate LLM driven analysis, code generation, testing, and review with explicit human approval gates and segregation of duties.
• Ensure LLM driven systems meet enterprise reliability and resilience expectations, including disaster recovery, fallback behaviors, regional resiliency, and performance SLOs.

 

Required qualifications, capabilities, and skills

• 1+ years of experience or equivalent expertise in troubleshooting, resolving, and maintaining information technology services
• Strong coding skills in Java/Python and SQL, applied to building LLM enabled micro services, retrieval pipelines, evaluators, and data tooling; solid understanding of data structures, algorithms, and object‑oriented programming as applied to LLM latency, caching, and throughput.
• Hands‑on experience with AWS and cloud data management (e.g., Redshift, Dynamo DB, Aurora, Data bricks), plus experience integrating managed model endpoints and embedding/vector services; familiarity with secure secret management, networking, and least‑privilege access.
• Proficiency in automation, CI/CD, and agile methodologies with LLM Ops extensions: prompt and config versioning, automated evaluations, canary releases, and rollback strategies.
• Experience in system design, application development, and operational stability for LLM architectures, including retrieval layers, vector stores, caching, observability, rate limiting, and backpressure strategies.
• Strong analytical, problem‑solving, and communication skills, including the ability to explain model behaviors, tradeoffs, and control decisions to both technical and non‑technical stakeholders.
• Provide L3 and BAU support for Markets by leveraging LLMs for incident triage, run book retrieval, and pre‑approved auto‑remediation, with on‑call coverage for LLM services and dependencies.
• Expert-level knowledge of how large language models work and hands-on experience training and fine-tuning approved models (e.g., Claude, Chat GPT and successors), with a proven track record integrating LLMs as controlled, reliable components of the software engineering lifecycle in regulated environments, ensuring determinism, reproducibility, safety, and traceability.
• Strong understanding of data modeling challenges in big data and LLM contexts, embeddings, chunking strategies, vector similarity nuances, retrieval quality measures, and document lineage.

 

Preferred qualifications, capabilities, and skills

• Define model usage guidelines outlining which models are appropriate for requirements analysis, code generation and refactoring, test generation, documentation and explanation, and lead the use of LLMs for structured requirements analysis, translating business and regulatory requirements into clear technical specifications and control implementations.
• Establish best practices for prompt driven design and development, treating prompts and system instructions as versioned, reviewable engineering artifacts and ensuring change control and traceability, ensure prompt strategies support determinism, reproducibility, and traceability in regulated environments (e.g., seeded examples, constrained decoding, output schemas, and canonical evaluation sets), and oversee prompt libraries and reusable patterns aligned with enterprise coding and architectural standards, including shared retrieval components and guardrail policies.
• Ability to continuously learn the new developments happening in Agentic AI and LLM driven software coding

 

 

JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Experience Level

Entry Level