Molecular Connections
Website:
molecularconnections.com
Job details:
Location: Hybrid
Experience: 6–9+ years
Role Type: Full-time | Individual Contributor / Technical Lead
About the Role
We are looking for a Senior Backend Engineer who takes end-to-end technical ownership of backend systems handling large-scale data and production AI agentic workflows.
You will not simply receive task lists—you will partner with product and architecture teams to define problems, design robust systems from scratch, and eliminate technical debt. Crucially, you possess a first-principles, disciplined approach to engineering: when faced with performance bottlenecks, high-throughput scaling issues, or system failures, you rely on systematic profiling, benchmarking, and telemetry rather than trial-and-error.
Responsibilities
- End-to-End Ownership: Take business and product requirements from ambiguity to scalable, production-ready backend systems and APIs without needing step-by-step task breakdown.
- Performance Engineering & Optimization: Systematically profile, benchmark, and optimize Python applications, data pipelines, and database queries for low latency, high throughput, and memory efficiency.
- AI Agent Orchestration: Architect and maintain resilient agentic workflows (multi-step reasoning chains, dynamic tool calling, agent-to-agent communication, and autonomous task execution) integrated with leading LLM APIs.
- Data Architecture: Design, optimize, and maintain hybrid database architectures across PostgreSQL and MongoDB; develop high-performance search and retrieval indexes (OpenSearch/Elasticsearch).
- Systematic Problem Solving: Lead production debugging and incident triage using observability tools, distributed tracing, and profiling rather than ad-hoc guessing.
- Engineering Standards: Drive code review rigor, test strategies (unit, integration, load testing), and CI/CD best practices across the team.
Requirements
- 6+ years of professional backend software development experience, with proven experience operating at a Senior or Lead level.
- Deep Mastery of Python: Comprehensive understanding of Python internals, including execution models, the GIL, memory allocation, multi-threading vs. multiprocessing vs. asyncio event loops, and asynchronous web frameworks (FastAPI, Starlette, or modern Django/Flask).
- Disciplined Performance & Profiling Skills: Demonstrated ability to systematically isolate and resolve latency and resource bottlenecks using profiling tools (e.g., cProfile, py-spy, memory_profiler, APM distributed traces).
- Database & Query Optimization: Deep expertise in PostgreSQL and MongoDB—expert understanding of schema design, composite indexing, execution plans (EXPLAIN ANALYZE), lock contention, and connection pooling.
- Production AI / LLM Orchestration: Hands-on experience architecting agentic workflows and tool-calling systems in production using frameworks such as LangGraph, CrewAI, LlamaIndex, or custom orchestration runtimes.
- Large-Scale Data Handling: Proven background designing services and pipelines that handle high-concurrency, high-throughput data processing and search indexing (OpenSearch or Elasticsearch).
- Autonomous Work Ethic: Track record of driving technical initiatives independently, defining architecture patterns, and proactively identifying bottlenecks before they impact production.
Nice to Have
- Experience with streaming/message brokers (Kafka, RabbitMQ) and caching topologies (Redis).
- Experience with agent evaluation, LLM tracing, and guardrail frameworks (e.g., Langfuse, Phoenix, TruLens).
- Familiarity with Docker, Kubernetes, and AWS infrastructure (ECS, EKS, RDS, OpenSearch Service).
Click on Apply to know more.
