company-logo
AI / ML / LLM Engineer | Python | PyTorch | Hugging Face | Agentic AI

Description

Our client is an established technology company developing advanced voice and data-processing solutions for telecommunications providers and government organisations. Its platforms process large volumes of IP, voice and communications data, supporting complex analytics and operational use cases across international markets. The company is now looking for an experienced AI/ML Engineer to help develop, fine-tune and deploy advanced machine-learning capabilities across natural language processing, large language models and related data modalities. 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀 • Design, fine-tune and evaluate Large Language Models for applications such as text classification, entity extraction, summarisation, sentiment analysis and conversational AI. • Build and improve domain-specific training datasets, including data preparation, synthetic-data generation, annotation quality and validation. • Develop reliable machine-learning pipelines for text processing, model training, evaluation and production inference. • Apply parameter-efficient fine-tuning techniques such as LoRA, QLoRA, adapters, prompt tuning and instruction tuning. • Explore Agentic AI patterns and integrate models with external tools, data sources and enterprise workflows. • Develop robust model-evaluation frameworks using automated benchmarks, qualitative review and human-in-the-loop assessment. • Improve model performance through quantisation, pruning, distillation, inference optimisation and efficient deployment. • Collaborate with engineering, product and technical teams to integrate AI capabilities into secure production environments. • Evaluate emerging foundation models and recommend suitable architectures for specific product and customer requirements. • Contribute to reproducible ML development practices, experiment tracking, technical documentation and production monitoring. 𝗧𝗵𝗲 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 This role offers the opportunity to work on technically challenging AI applications involving large-scale communications data, language processing and advanced model deployment. The successful candidate will contribute to AI capabilities used in complex telecommunications and government environments while working with an international team on emerging applied-AI use cases. Occasional travel may be required for customer meetings, conferences or collaboration with other offices.

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics or a related discipline.
  • Four or more years of experience in machine learning engineering, applied AI or deep-learning development.
  • Strong proficiency in Python, PyTorch and the Hugging Face ecosystem.
  • Practical experience working with transformer architectures and Large Language Models.
  • Hands-on experience with model fine-tuning, evaluation and performance optimisation.
  • Experience with techniques such as LoRA, QLoRA, PEFT, instruction tuning or comparable adaptation methods.
  • Good understanding of tokenisation, embeddings, attention mechanisms, context management and LLM inference.
  • Experience deploying machine-learning models into production environments.
  • Familiarity with Docker, model tracking, version control and reproducible ML workflows.
  • Strong analytical and problem-solving skills, with the ability to evaluate model quality and improve performance.
  • Excellent written and spoken English.

Desirable

  • Agentic AI and tool-enabled workflows
  • Model Context Protocol integration
  • Automatic Speech Recognition and speech-to-text
  • Whisper, Wav2Vec2 or related speech models
  • Multimodal models combining text, audio or visual information
  • Diffusion models and synthetic-data generation
  • ONNX, TensorRT, quantisation or inference optimisation
  • Distributed or multi-GPU model training
  • Kubernetes, MLflow, Ray or similar MLOps technologies
  • Real-time inference and streaming-data environments
  • Privacy-preserving, explainable or security-sensitive AI
  • Telecommunications, voice analytics or communications-data applications
Role Highlights
💰 Compensation
USD 80K-90K/year + benefits structure
📍 Location
Portugal
💼 Work Location Type
Remote
📄 Employment Type
Full-time
📈 Job Level
Senior
⌛ Experience
8+ years
🏢 Department
Information Technology
🏭 Industry
Technology, Information & Media
🔹 Sub-Industry
AI Development
Getting StartedA few quick details so we know how to reach you
How did you hear about us? *
Which country's passport do you hold? *
Email *(Please ensure the email matches the one mentioned in your CV or resume)
LinkedIn Profile URL *
Please mention your notice period *
Let’s Get to Know You BetterA few short questions to understand your experience and what you enjoy doing
1. Does your CV clearly demonstrate at least four years of professional experience building machine-learning or deep-learning solutions? Please update your CV to show the relevant employers, roles and dates before applying. *
2. Does your CV include at least one production application that you personally built or deployed using Large Language Models or transformer-based models? Please identify the project and your contribution in your CV. *
3. Does your CV clearly demonstrate hands-on experience fine-tuning or adapting an LLM using methods such as LoRA, QLoRA, PEFT, instruction tuning or adapters? *
4. Does your CV demonstrate strong hands-on experience with Python, PyTorch and the Hugging Face ecosystem in a professional or substantial production project? *
5. Have you personally built an Agentic AI solution in which an LLM used tools, APIs, enterprise data sources or external systems to complete multi-step tasks? Please ensure this project is described in your CV. *
Final DetailsSalary expectations and any supporting credentials
1. Where does your salary sit today?*
Enter your current annual salary in USD
2. What’s the number that’ll make you say "this is worth it"?*
Enter your target annual salary in USD
3. Which AI or LLM project are you most proud of, and what made it technically or commercially meaningful?*
Describe the problem, what you personally built, the models or tools used, and the outcome achieved.
4. Tell us about a model you fine-tuned, adapted or significantly improved. What approach did you take, and how did you know it worked?*
You may reference techniques such as LoRA, QLoRA, PEFT, instruction tuning, evaluation benchmarks or human review.
5. Describe the most interesting Agentic AI workflow you have built or contributed to. What could the agent do, and which tools, APIs or data sources did it use?*
Focus on your personal contribution, the architecture and how the workflow performed in practice.
6. What is the toughest production challenge you have solved when deploying an AI model, and how did you overcome it?*
Examples may include latency, cost, hallucination, scaling, privacy, monitoring, model drift or infrastructure constraints.
7. Which methods have you used to evaluate the quality of an AI or LLM system?*
Select every method you have personally implemented or managed.
Upload ResumeHelp us get to know you better by sharing your most recent resume
Frequently Asked Questions (FAQs)Have a question? Check here for quick answers

This confidential recruitment process is being managed by byteSpark.ai on behalf of an established international technology organisation operating in highly specialised, mission-critical environments. byteSpark.ai is not the employer for this position. Further information about the company, engineering leadership, products, and AI roadmap will be shared with shortlisted candidates at the appropriate stage.

This is an opportunity to work on applied AI problems where reliability, performance, security, and real-world usability genuinely matter. The successful candidate will help shape AI capabilities involving Large Language Models, Agentic AI, NLP, speech, and multimodal technologies within a complex enterprise product environment. The role offers the chance to influence how AI is designed, integrated, and operationalised rather than simply consuming third-party models.

The successful candidate will design, build, fine-tune, evaluate, and deploy AI capabilities that can be integrated into real products and operational workflows. This includes developing LLM and machine-learning services, connecting models to APIs and enterprise data sources, improving inference performance, evaluating reliability, and working closely with engineering and product teams to move AI initiatives from concept into production.

The client is not building generic AI demonstrations. Its technology operates in environments where systems must be dependable, secure, explainable, and capable of handling complex data and workflows. Engineers will therefore be exposed to meaningful technical challenges involving model quality, latency, integration, deployment constraints, and production reliability.

Candidates will stand out if they can show that they have personally taken AI systems beyond experimentation. Strong evidence of production LLM applications, model fine-tuning, Agentic AI workflows, enterprise integration, PyTorch, Hugging Face, MLOps, optimisation, and measurable technical or commercial outcomes will significantly strengthen an application.

The client is looking for a hands-on engineer who can move confidently between model development, software engineering, experimentation, and production delivery. The ideal candidate is intellectually curious, technically rigorous, comfortable taking ownership, and motivated by solving difficult applied AI problems rather than working only on research or isolated prototypes.

The role is expected to offer meaningful technical ownership. The successful candidate will contribute to architecture decisions, model selection, experimentation, deployment approaches, and the development of reusable AI capabilities. This is not a narrow implementation role; the engineer will help shape how the organisation applies AI across its product environment.

The position is based in Portugal. Compensation will be aligned with the candidate's experience, technical depth, and overall fit for the role. Further details regarding salary, benefits, working arrangements, and any applicable relocation support will be discussed with shortlisted candidates during the recruitment process.