Senior Applied AI / ML Engineer

About the role

iwconnect is looking for a Senior Applied AI / ML Engineer to join its AI Solutions Delivery Team. The role covers research, development and implementation of advanced AI and machine learning solutions, working with traditional machine learning, large language models (LLMs), knowledge graphs and agentic AI. The engineer turns successful experiments into reliable, production-ready solutions.

Key responsibilities

• Research and experiment with emerging AI/ML technologies and frameworks.
• Design, develop and implement AI/ML solutions in Python, applying machine learning techniques and evaluating different models.
• Explore and implement LLM-based, agentic and hybrid AI solutions, and work with knowledge graphs, ontologies and semantic modeling.
• Build proof of concepts, evaluate results and recommend technical solutions.
• Help move AI/ML experiments into scalable, production-ready applications using MLOps/LLMOps practices (model tracking, versioning, deployment, monitoring).
• Collaborate with software engineering, data, cloud/DevOps, QA and architecture teams, and share technical findings with internal teams and clients.

Required profile

• Strong Python skills and experience writing production-quality code.
• Solid machine learning fundamentals, including classification, regression, clustering and model evaluation.
• Experience with ML libraries such as scikit-learn, Pandas/Polars, PyTorch or similar.
• Practical experience with LLMs, embeddings and foundation models, and the ability to compare and evaluate approaches.
• Knowledge of knowledge graphs, ontologies and semantic modeling, with experience using Neo4j, RDF/OWL, SPARQL or similar.
• Hands-on experience with agentic AI and tool-using systems, with familiarity with tools such as Claude Code, GitHub Copilot or Cursor.
• Understanding of MLOps/LLMOps, with experience using tools such as MLflow or Kubeflow.
• Strong R&D mindset, excellent communication skills, and excellent spoken and written English, including confidence in client-facing discussions.

Nice to have

• Experience with RAG, semantic search and vector databases.
• Experience with open-source or local models, or model fine-tuning.
• Experience with cloud AI services such as Azure, AWS or GCP.
• Familiarity with Docker, Kubernetes and container-based AI workloads.