Senior Software Engineer - AI/ML
Mitratech, which builds software for Legal, Risk, Compliance, and HR functions, is hiring a Senior Software Engineer to design production-grade GenAI systems: multi-agent workflows, end-to-end RAG pipelines, and LLM evaluation frameworks. Core stack is Python on AWS (Bedrock, SageMaker, OpenSearch) with Terraform/CDK and MLOps practices.
- Compensation
- Not disclosed
- Location
- Germany - Remote
- Work setup
- Remote
- Seniority
- Senior
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- Production experience designing multi-agent systems with tool use, memory/state management, and fault-tolerant routing
- Familiarity with LangChain, LangGraph, AutoGen, or custom orchestrators
- Hands-on experience building RAG pipelines end-to-end (chunking, embedding models, vector databases, retrieval tuning, answer synthesis) at production scale
- Strong experience defining and running evaluation pipelines for generative AI: automated scoring, human evaluation design, hallucination mitigation, drift monitoring
- Experience with foundation models and GenAI providers (AWS Bedrock, OpenAI, Anthropic, Meta), including fine-tuning, instruction tuning, and prompt engineering at scale
- Solid grounding in classical NLP techniques (NER, text classification, intent detection, topic modelling) and judgment on when to apply them vs LLMs
- Hands-on experience with Amazon Bedrock: foundation model APIs, Bedrock Agents, Knowledge Bases, and Guardrails
- Proficiency with AWS ecosystem: SageMaker, Lambda, ECS/EKS, S3, OpenSearch, IAM, CloudWatch, VPC networking
- Familiarity with MLOps: model registries, CI/CD for ML, feature stores, canary deployments, monitoring, rollback
- Experience with Terraform or AWS CDK for infrastructure-as-code
- Production-quality Python: packaging, pytest, type hints, async programming, clean ML pipeline abstractions
- Familiarity with traditional ML frameworks and fine-tuning workflows
- Experience with experiment tracking tools (Langfuse, Arize, LangSmith, or equivalent)
Nice to have
- LLM-as-judge patterns
- Experience with Bedrock Model Evaluation
Full description
At Mitratech, we are a team of technocrats focused on building world-class products that simplify operations in the Legal, Risk, Compliance, and HR functions. We are a close-knit, globally dispersed team that thrives in an ecosystem that supports individual excellence and takes pride in its diverse and inclusive work culture centered around great people practices, learning opportunities, and having fun! Our culture is the ideal blend of entrepreneurial spirit and enterprise investment, enabling the chance to move at a rapid pace with some of the most complex, leading-edge technologies available.
For over 35 years, the experts at Mitratech have been focused on solving complex needs. Today, we serve 20,000 client companies of all sizes globally, representing 30% of the Fortune 500 and over 500,000 users in over 160 countries.
As we continue to grow, we’re always looking for resourceful, enthusiastic, and fresh perspectives. Join our global team and see what makes Mitratech a truly exceptional place to work!
Given our continued growth, we always have room for more intellect, energy, and enthusiasm - join our global team and see why it's so special to be a part of Mitratech!
Job Overview
We are seeking a highly skilled Senior Software Engineer specialising in Generative AI and Large Language Models, with a strong focus on agentic systems, Retrieval-Augmented Generation, and AI evaluations, to join our dynamic team. The ideal candidate will play a pivotal role in architecting and delivering production-grade AI solutions that meet complex business objectives effectively. This position requires a blend of expertise in modern AI technologies and software engineering, along with a passion for staying at the forefront of advancements.
Essential Duties & Responsibilities:
- Design, build, and operate multi-agent workflows and tool-enabled agents, implementing orchestration logic, state management, safety guardrails, and fallback strategies for resilient production pipelines.
- Architect and maintain end-to-end RAG systems, covering document ingestion, chunking, embedding, vector retrieval, reranking, and answer synthesis with a focus on quality, attribution, and latency.
- Evaluate and integrate LLMs and GenAI services across cost, performance, and privacy dimensions, selecting the right mix of managed and in-house models.
- Develop, version, and optimise prompting strategies; implement automated prompt testing and regression tracking to maintain output quality and reliability.
- Define and own evaluation frameworks for generative outputs, including automated metrics, LLM-as-judge approaches, human evaluation protocols, hallucination detection, and drift monitoring.
- Apply classical NLP techniques where appropriate and maintain awareness of data distribution shifts that could impact model behaviour in production.