Senior Software Engineer - Agentic AI & Python

Key Responsibilities: Agentic Infrastructure & APIs: Design and build reliable, high-throughput backend services and APIs powering AI-driven customer workflows, tool use, orchestration, and context retrieval. Applied AI & Hallucination Mitigation: Solve complex applied AI challenges across RAG, context grounding, model selection, hallucination reduction, and hybrid/deterministic logic vs. LLM execution. Evaluation Frameworks: Develop automated and human-in-the-loop evaluation strategies, evaluator calibration frameworks, regression suites, and production signal metrics. Production Guardrails & Reliability: Implement production guardrails, fallbacks, retry logic, observability, and failure-handling mechanisms for agentic systems. Distributed Workflows: Build asynchronous and distributed processing workflows (e.g., event queues, task systems) capable of processing high-volume AI workloads. Customer & Cross-Functional Collaboration: Partner directly with external customers, product managers, and cross-functional teams to translate feedback and workflow friction into production technical solutions. Operational Excellence: Participate in the team's on-call rotation to diagnose, mitigate, and conduct postmortems on production incidents. Experience & Education5–7 years of professional software engineering experience with a strong foundation in backend systems and distributed cloud applications. Hands-on, practical experience building and deploying generative AI or agentic AI applications into production for real users.

Technical Capabilities: Strong proficiency in Python and modern backend application frameworks (e.g., FastAPI, Django).Hands-on experience with asynchronous processing, event-driven architectures, and distributed task queues (Celery, Kafka, AWS SQS, RabbitMQ, or Redis).Deep understanding of database design, data modeling, REST/gRPC APIs, and persistence patterns. Comfort operating within cloud-native environments (AWS, Docker/containers, Kubernetes, infrastructure-as-code, CI/CD pipelines).Proven ability to evaluate tradeoffs across quality, latency, cost, reliability, and architectural complexity.

Preferred Qualifications: Experience training, fine-tuning, distilling, or adapting machine learning and language models for production workflows. Hands-on experience with reinforcement learning or feedback-driven optimization techniques. Demonstrated background engineering AI evaluation platforms (e.g., LLM-as-judge systems, benchmark suites, quality metrics platforms).Experience optimizing inference cost, latency, or throughput for AI systems operating at significant scale.