Technology support I

Job Responsibilities

• Troubleshoot and monitor production application flows to ensure end-to-end application or infrastructure service delivery to support business operations, addressing anomalies using standard observability tools.
• Uses enterprise-authorized AI capabilities within the work environment to speed up incident triage and initial problem analysis (e.g., summarizing logs/symptoms into hypotheses), validating outputs and handling operational data according to sensitivity and security requirements.
• Assist in the improvement of operational stability and availability through participation in problem management.
• Identify and document basic issues and potential solutions and support the management of incidents, problems, and changes in technology applications or infrastructure, escalating in compliance with firm policy and processes.
• Applies reuse-first, AI-assisted practices within operational stability routines to identify recurring interruption patterns and support validated remediation actions aligned to resiliency and security expectations.
• 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.
• 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.
• 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.
• Create secure, high-quality production code: implement LLM-assisted microservices, 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.

• Drive LLMOps 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 while ensuring LLM-driven systems meet enterprise reliability and resilience expectations (disaster recovery, fallback behaviors, regional resiliency, and performance SLOs).

  
  

Required Qualifications, Capabilities, and Skills

• Formal training or certification on troubleshooting, resolving, and maintaining information technology services concepts and advanced applied experience
• Working knowledge of using enterprise-authorized AI capabilities within the work environment to support production support workflows with strong validation habits and awareness of data sensitivity.
• Ability to review and validate AI-assisted incident recommendations before action, escalating when uncertain and following operational and security expectations.
• Familiarity with applications or infrastructure in a large-scale technology environment on-premises or in the public cloud.
• Formal training or certification in software engineering concepts, with practical experience applying them to LLM-enabled systems in regulated environments.
• Strong coding skills in Java/Python and SQL, applied to building LLM-enabled microservices, 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, DynamoDB, Aurora, Databricks), 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 LLMOps 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.

• Strong understanding of how large language models work and of data-modeling challenges in big data and LLM contexts — embeddings, chunking strategies, vector similarity nuances, retrieval quality measures, and document lineage.