Manager of Artificial Intelligence

We are looking for a hands-on AI Architect who bridges software development, data engineering, and advanced AI/ML. You will design and code AI solutions that leverage large language models, embeddings and retrieval‑augmented generation while ensuring robust data pipelines and integration with our software products. This role requires a strong foundation in software engineering and data engineering along with deep expertise in AI/ML.

Key Responsibilities:

Cross‑Disciplinary Solution Design

• Engage with stakeholders to understand product requirements and architect end‑to‑end solutions incorporating data ingestion, model training, deployment and monitoring.
• Design systems that combine traditional software components with AI/ML models and modern data engineering practices.

Hands‑On Development

• Write and maintain production‑quality code across back‑end services, data pipelines and model inference layers, primarily using Python and at least one other language (e.g. .Net, Java).
• Build RESTful or gRPC APIs to expose AI functionality and integrate with front‑end and other microservices.

Data Engineering & Pipeline Construction

• Develop and manage data pipelines for ingestion, transformation and storage (using tools such as Apache Spark, Kafka, Airflow, SQL/NoSQL databases).
• Work with vector databases and embedding stores for retrieval‑augmented systems.
• Ensure data quality, schema management and robust ETL processes that support model training and inference.

Advanced AI/ML & LLM Expertise

• Train, fine‑tune and deploy machine‑learning and deep‑learning models, including large language models (LLMs) and transformer architectures (e.g., GPT, Llama, Mistral).
• Implement embedding techniques (e.g., sentence and document embeddings), retrieval‑augmented generation (RAG) pipelines and hybrid search (vector + keyword).
• Build customised pipelines using frameworks like LangChain, LLamaIndex or similar to integrate models with diverse data sources.

System Optimisation & Scalability

• Optimise model performance (latency, throughput and accuracy) for production workloads, using techniques such as model quantisation, batching and caching.
• Scale services using containerisation (Docker) and orchestration (Kubernetes) on cloud platforms (AWS, Azure, GCP).

Collaboration & Mentorship

• Collaborate with software engineers, data scientists and DevOps teams to deliver cohesive solutions.
• Mentor junior engineers and share best practices in software development, data engineering and AI/ML.

Monitoring & Maintenance

• Implement observability for data pipelines and AI services (logging, metrics, model drift detection).
• Continuously monitor and iterate models and pipelines based on feedback and new data.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, Artificial Intelligence or related field.
• Experience:
  Minimum 6–8 years in software development and data engineering, with at least 3 years of hands‑on AI/ML experience.
• Proficiency in Python and deep familiarity with AI/ML frameworks (e.g., PyTorch, TensorFlow, Scikit‑learn) and LLM libraries (e.g., Hugging Face Transformers, LangChain).
• Hands‑on experience building and deploying large language models, embeddings, RAG pipelines and vector‑database‑powered applications.
• Strong knowledge of data engineering tools (Spark, Kafka, Airflow), relational and NoSQL databases, and proficiency in SQL.
• Experience designing and operating microservices, APIs and containerised applications on cloud platforms.
• Solid understanding of MLOps best practices (CI/CD for ML, model versioning, automated testing, monitoring).
• Strong problem‑solving and communication skills; ability to translate business requirements into technical designs.

Preferred Qualifications

• Experience with GPU orchestration (NVIDIA Triton, KServe) for model serving.
• Knowledge of privacy‑preserving techniques and responsible AI practices.
• Exposure to reinforcement learning or recommendation systems.