Senior Application Engineer

Responsibilities

• Design, build, and operate backend services, APIs, and application components that power AI Accelerator products.
• Develop Python/FastAPI, Type Script/Node, or similar services that integrate LLM APIs, retrieval systems, workflow engines, and internal enterprise systems.
• Execute AI Accelerator cycles of six two-week sprints over a 12-week cycle by developing, testing, and validating cloud and agentic AI product increments.
• Develop MCP-accessible services that allow approved agents to read, write, search, and maintain structured (e.g. markdown/YAML) knowledge assets.
• Build MCP/FastMCP read-write-search APIs, permissioned knowledge stores, version control, audit trails, access controls, and integrations with AWS-native storage and identity patterns.
• Implement secure application patterns for authn/authz, BMS SSO, BMS Cloud Creds, secrets management, auditability, input validation, and safe service boundaries.
• Partner with frontend engineers to define clean API contracts, streaming response patterns, error handling, and service-level behaviors for AI-powered user experiences.
• Build and host agentic workflows using Lang Graph, including workflow state, multi-agent orchestration, tool execution, fan‑out/fan‑in patterns, and durable checkpoints.
• Develop MCP tool integrations and FastMCP servers that allow agents to use governed enterprise capabilities safely and consistently.
• Implement retrieval, memory, and context services using AWS-aligned data stores such as S3, Athena, PostgreSQL/RDS, Elasti Cache/Redis, Open Search, Amazon S3 Vectors, and Amazon Neptune.
• Build and evolve the semantic layer for SQL and other natural‑language‑to‑code generating agents, enabling novel analytical questions to be grounded in query history, column values, warehouse context, explicit instructions, memory, and governed data tools.
• Create and maintain CI/CD pipelines, environment configuration, automated tests, infrastructure‑as‑code, and release processes for cloud AI applications.
• Instrument application reliability, latency, cost, usage, tracing, and model/agent behavior using enterprise observability and AI evaluation tools such as Lang Smith or similar platforms.
• Embedding automated quality gates, security scans, regression tests, structured output validation gates, and responsible AI guardrail checks into delivery pipelines.
• Continuously improve shared platform patterns based on lessons learned across pods, changing enterprise standards, and advances in AI engineering practices.
• Partner with AI Engineers, Data Engineers, Data Scientists, Frontend Engineers, Pod Leads, architects, and product teams to solve complex delivery challenges.
• Help complete MVP transition activities by maturing AI capabilities, adding key features, validating reliability in practice, confirming business value, and assessing production readiness.
• Provide technical coaching through design reviews, code reviews, architecture reviews, incident learning, documentation, and reusable examples.

• Bachelor's or higher degree in Computer Science, Engineering, Science, or a related field.
• 5+ years of experience in software engineering, cloud engineering, platform engineering, or backend application development with increasing responsibility.
• Hands‑on experience building cloud‑native applications on AWS; familiarity with services such as S3, RDS/PostgreSQL, Athena, Elasti Cache/Redis, Open Search, Fargate, Lambda, IAM, and VPC patterns.
• Strong proficiency in Python, FastAPI, Type Script/Node, or comparable backend application frameworks.
• Experience with containers, CI/CD, Git Hub‑based workflows, automated testing, environment configuration, and infrastructure‑as‑code such as Terraform, AWS CDK, or Cloud Formation.
• Experience building LLM, RAG, or agentic AI applications using frameworks such as Lang Graph, Lang Chain, PydanticAI, Claude Agent SDK, or similar tools.
• Familiarity with MCP/FastMCP, read‑write‑search APIs, permissioned markdown/YAML stores, vector databases, knowledge graphs, session/state management, structured output validation gates, and evaluation‑driven development.
• Experience with SQL, semantic layers, data warehouse context,…