Senior Software Engineer - AI/ML
Remote ✓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. • Build and operate scalable, secure AI infrastructure on AWS (Bedrock, SageMaker, Lambda, OpenSearch), following well architected principles and infrastructure-as-code practices. • Own the full deployment lifecycle: CI/CD for models and agents, testing strategies, observability, and rollback procedures. • Ensure data quality through rigorous validation and augmentation, and proactively source datasets for training, fine-tuning, and evaluation. Requirements & Skills: • Agent Orchestration: Production experience designing multi-agent systems with tool use, memory/state management, and fault-tolerant routing. Familiarity with LangChain, LangGraph, AutoGen, or custom orchestrators. • RAG & Retrieval: Hands-on experience building RAG pipelines end-to-end: chunking, embedding models, vector databases, retrieval tuning, and answer synthesis at production scale. • Evaluations: Strong experience defining and running evaluation pipelines for generative AI — automated scoring, human evaluation design, hallucination mitigation, and drift monitoring. LLM-as-judge patterns are a plus. • LLMs & GenAI: Demonstrated experience with foundation models and GenAI providers (AWS Bedrock, OpenAI, Anthropic, Meta). Comfortable with fine-tuning, instruction tuning, and prompt engineering at scale. • Traditional NLP: Solid grounding in classical NLP techniques (NER, text classification, intent detection, topic modelling) and good judgement on when to apply them alongside or instead of LLMs. • AWS Bedrock: Hands-on experience with Amazon Bedrock: foundation model APIs, Bedrock Agents, Knowledge Bases, and Guardrails. Experience with Bedrock Model Evaluation is a plus. • AWS Ecosystem: Proficiency with SageMaker, Lambda, ECS/EKS, S3, OpenSearch, IAM, CloudWatch, and VPC networking. • MLOps & CI/CD: Familiarity with model registries, CI/CD for ML, feature stores, canary deployments, monitoring, and rollback. • IaC: Experience with Terraform or AWS CDK for reproducible infrastructure provisioning. • Python: Production-quality Python: packaging, testing (pytest), type hints, async programming, and clean ML …
