The Opportunity:

We are seeking a Principal Architect AI/ML to lead the design, development, and deployment of scalable, production-grade AI/ML and GenAI solutions across our technology ecosystem. This is a highly technical, hands-on leadership role focused on building next-generation AI platforms, frameworks, and intelligent systems.

This is not a traditional managerial role. We are looking for a player-coach who is comfortable writing code, designing architectures, and mentoring engineering teams simultaneously.

What Youll Do:

AI/ML Architecture & Engineering:

- Architect and develop scalable, production-ready AI/ML systems across the full lifecycledata ingestion, modelling, deployment, and monitoring.

- Design modular AI platforms, reusable components, and ML frameworks to accelerate solution development.

- Lead the development of high-performance ML systems leveraging distributed computing and large-scale data processing.

Hands-on Development & Technical Leadership:

- Actively contribute to coding, model development, system design, and troubleshooting.

- Provide deep technical guidance to engineering and ML teams, ensuring best practices in design and implementation.

- Solve complex engineering challenges in model scalability, latency optimisation, and real-time inference systems.

AI/ML & GenAI Solution Development:

- Build and deploy advanced ML models, including supervised, unsupervised, and deep learning systems.

- Drive adoption of GenAI and LLM-based solutions (RAG, embeddings, prompt engineering, fine-tuning).

- Develop intelligent systems including document understanding & IDP, conversational AI, prediction/classification systems, and NLP-based automation.

MLOps & Platform Engineering:

- Establish and implement robust MLOps practices for model lifecycle management.

- Build automated pipelines for training, testing, deployment, monitoring, and retraining.

- Integrate ML workflows into CI/CD pipelines and cloud-native architectures.

Cloud & Distributed Systems:

- Architect solutions across multi-cloud environments (Azure, AWS, GCP).

- Leverage containerisation, Kubernetes, microservices, and serverless architectures for AI workloads.

- Design systems for scalability, fault tolerance, and high availability.

Data & Platform Readiness:

- Partner with data engineering teams to ensure high-quality, scalable data pipelines.

- Design feature stores, model serving layers, and data access patterns optimised for ML use cases.

Mentorship & Collaboration:

- Mentor and upskill teams of ML engineers and software developers.

- Collaborate with product, engineering, and platform teams to ensure successful solution delivery.

- Act as a technical anchor in architecture discussions, design reviews, and innovation initiatives.

What You Bring:

Experience & Education:

- 12+ years of experience in AI/ML, software engineering, or platform engineering roles.

- Bachelors or Masters degree in Computer Science, Engineering, Mathematics, or a related technical field.

- Cloud certifications (AWS, Azure, GCP) or specialised AI/ML credentials are a plus.

Technical Expertise:

- Proven experience designing and deploying AI/ML solutions in production environments.

- Strong hands-on expertise in Python, SQL, and distributed computing frameworks.

- Deep experience with ML libraries/frameworks (TensorFlow, PyTorch, Scikit-learn).

- Experience building end-to-end ML systemsnot just modelling.

- Proficiency with MLOps tools (MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI, Airflow).

- Hands-on experience with GenAI/LLMs (RAG, embeddings, fine-tuning, prompt engineering).

- Experience working with cloud platforms (AWS/Azure/GCP).

Key Competencies:

- Deep expertise in AI/ML architecture, distributed systems, and cloud-native engineering.

- Strong knowledge of system design, scalable architectures, data pipelines, and distributed systems.

- Familiarity with vector databases, knowledge graphs, or multimodal AI systems is a plus.

- Exposure to real-time ML systems or streaming architectures is a plus.

- A continuous learning mindset, especially in emerging AI and GenAI technologies.