Senior Software Engineer - AI/ML

Role overview

This Senior Software Engineer role focuses on architecting and delivering production-grade generative AI and LLM-powered solutions within legal, risk, compliance, and HR product lines. The work spans agentic systems, retrieval-augmented generation, evaluation frameworks, and scalable cloud infrastructure on AWS. It blends modern AI expertise with rigorous software engineering practices and close collaboration across engineering, product, and data teams.

Responsibilities

- Design, build, and operate multi-agent workflows and tool-enabled agents, implementing orchestration logic, state management, safety guardrails, and fallback strategies. - Architect and maintain end-to-end RAG systems covering ingestion, chunking, embedding, vector retrieval, reranking, and answer synthesis with attention to quality, attribution, and latency. - Evaluate and integrate LLMs and GenAI services across cost, performance, and privacy dimensions, mixing managed and in-house models. - Develop, version, and optimize prompting strategies, with automated prompt testing and regression tracking. - Define and own evaluation frameworks for generative outputs, including automated metrics, LLM-as-judge approaches, hallucination detection, and drift monitoring. - Build and operate scalable, secure AI infrastructure on AWS using Bedrock, SageMaker, Lambda, and OpenSearch, with infrastructure-as-code practices and full deployment lifecycle ownership.

Requirements

- Production experience designing multi-agent systems with tool use, memory and state management, and fault-tolerant routing, using frameworks such as LangChain, LangGraph, AutoGen, or custom orchestrators. - Hands-on experience building RAG pipelines end to end at production scale. - Strong experience defining and running evaluation pipelines for generative AI, including hallucination mitigation and drift monitoring. - Demonstrated experience with foundation models and GenAI providers, plus comfort with prompt engineering, instruction tuning, and fine-tuning at scale. - Proficiency across the AWS ecosystem including SageMaker, Lambda, ECS/EKS, S3, OpenSearch, IAM, CloudWatch, and VPC networking, with hands-on Amazon Bedrock experience. - Production-quality Python with packaging, testing, type hints, and async programming, plus familiarity with IaC tools such as Terraform or AWS CDK and experiment tracking tools like Langfuse, Arize, or Langsmith. - Master's degree in Machine Learning or Computer Science, with a preference for NLP specialization.