Senior AI/ML Engineer

team.blue

. Architect and evolve the multi-agent orchestration platform, including plugins, tool-use pipelines, observability hooks, and voice, telephony, and messaging channel adapters .

Posted 10/20/2026full-timeRemote • Belgium, Italy, GermanySeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates extensive expertise in ML Engineering and Applied AI, with a strong focus on LLMs, MLOps, and AI observability. Capable of leading teams, mentoring engineers, and translating complex business problems into effective ML solutions.

Highest-signal resume keywords

LLM Fine-TuningPython ProgrammingMLOps PracticesAI ObservabilityMulti-Agent Coordination

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

LLM Inference ServingPrompt EngineeringReal-Time Streaming TTSVAD IntegrationRAG Pipeline DevelopmentModel Lifecycle ManagementAutomated EvaluationsData Ingestion PipelinesCI/CD for MLTool-Use Orchestration

Soft Skills

Strong Communication SkillsMentoring

Tools & Technologies

PyTorchHuggingFace TransformersMLflowDockerKubernetesLangfuseTensorRT-LLMVLLMW&BPEFT/LoRA

Industry Keywords

AI ArchitectureSpeech AIObservability HooksLatency-Sensitive ProductionData Leakage MitigationFairness and PrivacyMulti-Hop RAGQuantization TechniquesVoice PipelinesAgentic Frameworks

Tech Stack

Tools & technologies

DockerKubernetesPythonPyTorch

About the role

Key responsibilities & impact

Architect and evolve the multi-agent orchestration platform, including plugins, tool-use pipelines, observability hooks, and voice, telephony, and messaging channel adapters
Design and implement voice AI pipelines using STT, real-time streaming TTS, VAD, and SIP/RTP telephony integration with sub-300 ms latency targets
Build and maintain RAG pipelines with retrieval quality measurement, re-ranking, and hybrid vector and keyword search
Define MCP server architecture and tool-use contracts for internal and third-party integrations
Fine-tune and evaluate LLMs using LoRA, QLoRA, and DPO for customer support, classification, and structured extraction
Benchmark model quality using automated evaluations, human preference data, and speech/RAG-specific metrics
Manage model lifecycle, experiment tracking, versioning, reproducibility, and production promotion
Own AI observability with Langfuse tracing, LLM call instrumentation, cost tracking, and quality regression alerting
Define and enforce guardrails for hallucination detection, PII redaction, output safety scanning, and rate limiting
Build data ingestion, preprocessing, feature, and continual-learning pipelines
Drive ML CI/CD, including automated evaluation gates, shadow deployments, canary releases, and rollback triggers
Set AI architectural standards, conduct design reviews, and own ADRs
Mentor ML engineers and applied scientists
Collaborate with Product and Commercial teams to translate business problems into ML formulations and success metrics
Engage with external research partners and monitor emerging research and open-source developments

Requirements

What you’ll need

8+ years in ML Engineering, Applied AI, or Research Engineering
At least 2 years in a lead or staff-level role
Deep, hands-on production experience with LLMs, including fine-tuning, RLHF/DPO, prompt engineering, RAG, and tool use
Fluent in Python and experienced with PyTorch, HuggingFace Transformers, and PEFT/LoRA
Experience with LLM inference serving using vLLM, TensorRT-LLM, or TGI in a latency-sensitive production environment
Practical knowledge of agentic frameworks, multi-agent coordination, tool-call orchestration, context/memory management, and observability
Solid understanding of MLOps, including MLflow/W&B, Docker/Kubernetes, model registries, and CI/CD for ML
Awareness of LLM-specific risks and production mitigations, including hallucination, prompt injection, data leakage, fairness, and privacy
Strong communication skills for design documentation, architecture reviews, and explaining tradeoffs to non-technical stakeholders
Proof of eligibility to work in the country applied for
Experience with speech AI or real-time audio systems is a strong plus
Experience with voice pipelines, multi-hop RAG, MCP, open-source ML contributions, multimodal models, or quantization techniques is nice to have

Benefits

Comp & perks

No relocation packages or visa sponsorship
Diversity and inclusion commitment
ESG and sustainability initiatives