Senior Machine Learning Engineer

Responsibilities

• Generative AI & NLP: Architect and implement advanced language models for tasks such as summarization, semantic search, question answering, and classification using LLMs (GPT, LLaMA, Mistral).
• Model Customization: Fine-tune and optimize large language models using LoRA, PEFT, RAG, etc., for domain-specific tasks; ensure models are efficient and deployable in production.
• Production ML Engineering: Design and deploy robust ML pipelines using AWS (SageMaker, Lambda, Step Functions, EC2) and MLOps tools like MLflow, Docker, Airflow.
• CI/CD & Monitoring: Implement CI/CD workflows and observability for deployed models to ensure uptime, performance, and continuous improvement.
• Cross-functional Collaboration: Work closely with product, engineering, and data teams to define, develop, and deliver intelligent features and ML services.
• Scalability & Performance: Ensure your solutions scale efficiently across millions of requests and meet SLAs for latency, throughput, and reliability.
• Client & Stakeholder Engagement: Communicate technical concepts effectively with both technical and non-technical stakeholders; provide thought leadership and guidance.

Required Qualifications:

• 5+ years of hands-on experience in Machine learning, with a strong focus on NLP and/or LLMs
• Deep expertise with Python and ML/NLP libraries (e.g., Hugging Face, spaCy, NLTK, scikit-learn)
• Strong production experience with AWS cloud services, especially SageMaker, Lambda, Step Functions, and S3
• Proven track record deploying and monitoring ML models in production
• Familiarity with MLOps practices: model tracking, reproducibility, testing, A/B testing, and rollback strategies
• Experience with containerization (Docker, Kubernetes) and CI/CD pipelines for ML
• Strong problem-solving skills, systems thinking, and attention to detail
• Excellent communication and documentation skills

Preferred Qualifications:

• Experience with Retrieval-Augmented Generation (RAG), prompt engineering, or LLMOps
• Knowledge of vector search/retrieval systems (e.g. FAISS, Weaviate)
• Understanding data privacy, responsible AI practices, and model interpretability

What We Offer:

• A competitive salary and comprehensive benefits package
• 23 days of paid vacation
• Ongoing opportunities for professional growth and career advancement
• Hybrid workplace with occasional visits to the office in Tuzla/Sarajevo.
• A supportive, collaborative environment with passionate and skilled colleagues