Senior Full-Stack Engineer (Agentic AI Systems)

About the Role We are looking for a

Senior Full-Stack Engineer

who is highly technical, product-focused, and comfortable navigating architectural ambiguity. You will be responsible for building the user-facing digital application, engineering the integration interfaces, and developing the multi-agent orchestration layers. Working alongside our Senior Platform Engineers, you will translate advanced data science pipelines and machine learning guardrails into highly responsive, enterprise-grade digital experiences. Key Responsibilities 1. Backend Engineering & Multi-Agent Orchestration Develop Scoped AI Microservices:

Build and maintain the specialized backend logic for purpose-built agents (e.g., coverage explanation, policy comparison, and claims status workflows). Orchestrate Agent Workflows:

Engineer the mediating

Agent Advisor Layer

that brokers user intents, manages multi-agent workflows, and handles state synchronization using frameworks like LangGraph, AutoGen, or AWS Step Functions. Governed API Integration:

Develop secure microservices and API abstractions to bridge the gap between the AWS agentic layer and the carrier's legacy systems of record (CRM, Billing, Claims) without unmediated exposure. 2. Frontend Engineering & Conversational UX Build Next-Generation UIs:

Develop highly responsive, accessible web interfaces optimized for token-by-token text generation and interactive decision-support widgets. Optimize Streaming Protocols:

Implement Server-Sent Events (SSE) or WebSockets to sustain conversational streams under highly variable latency budgets. Human-in-the-Loop Integration:

Build operational dashboards and user routing flows that seamlessly escalate complex or unauthorized agent decisions to human advisors. 3. AI Platform Security, Guardrails & Observability Implement Platform Guardrails:

Programmatically enforce real-time input/output validation, PII filtering, and content boundaries directly within the application layer. Trace System Reasoning:

Integrate distributed tracing and custom application-level metrics into the codebase to reconstruct an agent's reasoning path (tools called, data fetched) for regulatory compliance. Secure Machine Identities:

Write application logic that respects short-lived, scoped credentials and distinct machine identities for agent-to-tool communications. 4. Delivery & CI/CD Excellence Automate Prompt & Code Testing:

Partner with platform teams to design automated evaluation suites within

Bitbucket Pipelines

that test agent behavior alongside standard unit/integration tests. Maintain Environment Parity:

Ensure clean configuration separation and application stability across Dev, Test, Staging, and Production tiers. Technical Skillset Requirements Programming Languages:

Deep proficiency in

Python

(FastAPI, Flask) for AI-orchestration and

Java

or

Node.js

for enterprise microservices stack alignment. Frontend Stack:

Modern JavaScript frameworks ( React.js , Next.js, or Angular) optimized for state management and high-performance rendering. AI & Orchestration Frameworks:

Experience with LangChain, LangGraph, AutoGen, or native cloud services like

AWS Bedrock Agents

and

AWS Step Functions . Databases & Vector Engines:

Hands-on experience with SQL/NoSQL databases (PostgreSQL, DynamoDB) and familiarity with Vector Datasets (OpenSearch, pgvector, Pinecone). Cloud & Operations (AWS):

Comfortable deploying applications inside AWS ecosystems (ECS, EKS, Lambda, API Gateway). Tools & Utilities:

Bitbucket/Git

for version control and CI/CD pipeline integration; Jira and Confluence for workflow tracking. Qualifications & Profile Experience:

8–12 years

of full-stack software development experience within fast-paced enterprise environments, digital product labs, or tech consulting firms. Domain Exposure:

Prior engineering experience in

FinTech, InsurTech, HealthTech , or highly regulated industries where data security and compliance are paramount is highly advantageous. Mindset:

Strong architectural curiosity, excellent cross-functional collaboration habits (working with data scientists and devops), and the ability to solve open-ended business problems.