Job Description:
This is an Onsite Role at Hartford, CT (3 days Onsite) About the Role: We're looking for engineers who can design and build production systems that combine strong software fundamentals with hands-on exposure to modern AI/ML tooling — specifically agentic AI workflows and Model Context Protocol (MCP) servers. You don't need to come from a pure AI/ML background; what matters is strong core engineering skills, sound architectural judgment, and the ability to pick up (or already know) how to integrate AI agents into real systems. This is a single posting covering both Senior and Lead levels — candidates with 4–7 years of experience are encouraged to apply. Must-Have Skills Python — strong, production-grade proficiency (primary language) Databases — solid relational/SQL fundamentals; schema design, query optimization, data modeling Cloud platform — strong hands-on experience with at least one of: Google Cloud Platform (Google Cloud Platform) (preferred), or AWS (acceptable alternative) 4+ years of professional software engineering experience Design & Architecture Thinking Required: Strong grounding in modular design — clear separation of concerns, well-bounded components/services, low coupling / high cohesion Ability to design systems that scale horizontally — statelessness where it matters, partitioning/sharding strategy, load distribution, avoiding single points of contention Working fluency in core design patterns and principles (e.g., SOLID, domain-driven boundaries, event-driven/async patterns, idempotency, caching strategies, API versioning) and knowing when to apply vs. avoid them Comfortable reasoning about tradeoffs: consistency vs. availability, latency vs. throughput, build vs. buy, synchronous vs. async workflows For Lead specifically: expected to drive these principles — set direction in design reviews, push back on designs that won't scale, and mentor others toward this way of thinking rather than just applying it. API & Real-Time Integration Best Practices Strong grasp of RESTful API design — resource modeling, versioning, pagination, idempotency for retries, proper status codes/error contracts Practical experience with WebSocket-based real-time communication — connection lifecycle management, reconnection/backoff strategy, heartbeat/keep-alive handling, and graceful degradation when a socket drops Understands tradeoffs between REST, WebSockets, and event-driven/streaming integration patterns, and picks the right one for the use case Follows security best practices for integrations — authentication/authorization (OAuth2, token scoping), input validation, rate limiting, and safe handling of external payloads Designs integrations for observability and resilience — structured logging, timeouts, circuit breakers, and clear failure modes rather than silent drops What We're Really Screening For Strong in Python + SQL/databases as a baseline Solid architectural instincts — modular, scalable design, not just "make it work" Proven in one major cloud (Google Cloud Platform strongly preferred, AWS acceptable) Comfortable or curious about Java (bonus, not blocker) Solid API/WebSocket integration practices — reliability, security, and observability by default Open to learning / already exposed to agentic AI + MCP server patterns Ideally has touched healthcare/health-tech systems before, but this is a plus, not a gate
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