Senior Software Engineer - AI/ML

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!

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.

• →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 pipeline abstractions.
• →ML Frameworks: Familiarity with traditional ML frameworks and fine-tuning workflows.
• →Experiment Tracking: Experience with Langfuse, Arize or Langsmith, or equivalent for tracking runs, metrics, and artefacts.
• →Ability to translate ambiguous business goals into concrete technical solutions and communicate tradeoffs to non-technical stakeholders.
• →Strong collaborative instincts — comfortable working across engineering, product, and data teams.
• →A rigorous, evidence-driven mindset: you ship with confidence because you measure, test, and monitor thoroughly.

• A Master’s degree in Machine Learning, Computer Science with a preference for specialization in the NLP domain.