Senior AI Engineer

The candidate will have responsibilities across the following functions:
Business Alignment and AI Solution Scoping (10%):
Engage with product and business stakeholders to identify high-value AI/ML opportunities.
Translate business objectives into AI solution designs, model success metrics, and technical strategies.
Prioritise use cases based on feasibility, data readiness, ROI, and ethical considerations.
Lead end-to-end model lifecycle: scoping, development, testing, deployment, monitoring, and re-training.
Data Engineering & Feature Pipeline Development (10%):
Collaborate with data engineers to design and build robust data pipelines (batch and streaming) to collect and curate large-scale structured and unstructured datasets.
Engineer high-quality features for AI models using advanced data processing technologies including signal processing, OCR, NLP techniques, or image preprocessing.
Ensure reproducibility, auditability, and traceability of training data.
AI Model Development and Optimisation (15%):
Design and implement quality AI/ML models using state-of-the-art AI technologies such as deep learning, generative models, graph neural networks, and reinforcement learning.
Train, validate, and fine-tune models for production-grade deployment using frameworks like LangChain, TensorFlow, PyTorch, Nemo, or Hugging Face.
Build reusable, modular components for faster experimentation and deployment to raise solution time-to-market.
Optimise models and systems for latency, throughput, and cost in production environments.
Integration, Deployment, MLOps and Infrastructure Automation (20%)
Develop APIs, microservices, and MCPs to integrate AI models into enterprise platforms and user-facing applications.
Collaborate with AI Architects to design scalable, real-time or batch AI workflows aligned to business requirements using advanced technologies (e. g., CI/CD, caching, model versioning).
Automate model deployment pipelines using MLOps tools (e. g. MLflow) and container orchestration platforms (e. g. Docker, Kubernetes, GitHub Actions, Terraform).
Ensure robust monitoring, version control, and rollback mechanisms for safe, repeatable AI deployments.
Operationalise models using AIOps or MLOps pipelines for training, testing, deployment and scaling (CI/CD, containerization, orchestration).
Build scalable APIs or model endpoints with low-latency inference using cloud-native tools and managed AI platforms (e. g., Azure AI, Databricks).
Monitor system performance, latency, cost, and retraining needs in production environments.
Manage model registry, lineage, and lifecycle across deployment stages.
Model Monitoring and Continuous Learning (10%):
Implement an end-to-end monitoring framework to ensure a single tracking-and-tracing process from business KPI, data quality and drift, to model performance.
Define retraining strategies, thresholds, and automation policies based on real-time or scheduled triggers.
Develop feedback loops including autonomous learning loops and optimised Human-in-the-Loop to enable model retraining and adaptation based on user behaviour or new data.
Track performance in production (accuracy, bias, latency) and manage model lifecycle in alignment with compliance needs.
Governance, Ethics and Compliance (5%):
Ensure AI models meet ethical AI guidelines, explainability requirements, and regulatory standards (e. g., GDPR, HIPAA, model fairness).
Document AI decision logic, limitations, risks, and assumptions for internal and external stakeholders.
Research and Innovation (20%):
Explore and prototype cutting-edge AI methodologies to drive innovation in core products and internal capabilities.
Stay current with academic and industry advancements in AI and contribute to technical knowledge sharing within the team.
Project Management and Collaboration (10%):
Lead or contribute to cross-functional AI projects across the full lifecycle, including scope definition, delivery tracking, risk mitigation, and stakeholder communication.
Collaborate across product, engineering, design, and operations to ensure successful AI integration and delivery.
Mentor junior engineers, enforce code quality, and lead technical delivery reviews.

Experience
7-11 yrs