0178AI Engineer

SUMMARY:
ESSENTIAL SKILLS:

• Deep understanding of classical and fundamental AI/ML algorithms (supervised, unsupervised, probabilistic
• models, Bayesian methods, optimization techniques).
• Strong experience designing and implementing machine learning models in production on AWS (SageMaker,
• EC2, Lambda, EKS).
• High proficiency in Python and key ML libraries (NumPy, SciPy, scikit-learn, TensorFlow/PyTorch, XGBoost/
• LightGBM).
• Solid software engineering skills: code quality, testing, CI/CD, reproducible experiments and version control
• (Git).
• Proven expertise in ML model evaluation, validation, bias detection, calibration and robustness testing.
• Extensive experience with data engineering and feature engineering: ETL, data pipelines, data quality, and use
• of AWS data services (Glue, Redshift, RDS, S3).
• Strong architecture skills for designing end-to-end AI solutions including model serving, inference scaling,
• monitoring and observability.
• Proven ability and keen willingness to mentor, coach and upskill junior engineers and data scientists, including
• running workshops and code reviews.
• Exceptional communication skills to translate technical concepts to stakeholders and vice verse, and produce
• high quality design documentation.
• Security, compliance and governance awareness for AI systems (data privacy, access control, model
• governance) and experience implementing controls in AWS.

• Solid hands-on experience with MLOps platforms and tools (SageMaker Pipelines, MLflow, Kubeflow, TFX).
• Deep knowledge of probabilistic programming frameworks (Pyro, Stan) or advanced Bayesian tooling.
• Experience with containerization and orchestration (Docker, Kubernetes/EKS).
• Familiarity with deep learning for structured and unstructured data (NLP, CV) even if focus remains on
• traditional methods.
• Experience with real-time or near-real-time inference architectures and streaming data (Kinesis, Kafka).
• Experience deploying models with cost-optimized inference patterns (spot instances, autoscaling, model
• shards).
• Familiarity with Explainable AI (XAI) techniques and tools (SHAP, LIME, model interpretability frameworks).
• Experience in building training programs, internal certifications or curriculum for upskilling technical teams.
• Certifications in AWS (e.g., AWS Certified Machine Learning - Specialty, Solutions Architect) or relevant
• industry certifications.
• Knowledge of domain-specific regulations (e.g., GDPR, HIPAA) and implementing privacy-preserving ML
• techniques (differential privacy, federated learning).

POSITION INFO:
ROLE & RESPONSIBILITIES:

• Lead the architecture, design and delivery of scalable, production-ready AI/ML solutions on AWS (or other tech
• stacks as per changing business requirements) with an emphasis on sound, classical AI techniques.
• Define and enforce best practices for ML model development, validation, deployment, monitoring and lifecycle
• management.
• Build and maintain end-to-end data and model pipelines using AWS native services ensuring reliability,
• reproducibility and maintainability.
• Conduct model selection, feature engineering and rigorous evaluation using statistical and probabilistic
• methods to ensure robust performance.
• Drive architectural decisions for model serving, inference scaling, rollback strategies and cost optimization.
• Implement and oversee model governance, versioning, monitoring, drift detection and alerting for production systems.
• Mentor and upskill junior data scientists and engineers through pair programming, technical reviews, structure training sessions and skills roadmap coaching.
• Create and deliver internal workshops, brown-bags and learning materials focused on foundational AI principle and practical implementation.
• Review and approve technical designs, code and proposals from team members, ensuring adherence to
• engineering standards and security controls.
• Collaborate closely with product managers, stakeholders and cross-functional teams to translate business problems into well-scoped ML solutions.
• Collaborate closely with product managers, stakeholders and cross-functional teams to translate business
• problems into well-scoped ML solutions.
• Lead post-implementation reviews and root cause analysis for incidents; drive continuous improvement and
• knowledge sharing.
• Advocate for ethical, transparent and explainable AI; ensure models are interpretable, fair and compliant with regulatory requirements.
• Willing and able to travel to global business partners as and when required.
• Any additional responsibilities assigned in the Agile Working Model Team Charter of the designated Product Sub-Product assigned to.

• Degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative discipline;
• advanced degree (MSc/PhD) preferred.
• Minimum 10 years of hands-on experience in AI/ML engineering and architecture, with at least 3 years
• deploying and operating models in AWS production environments.
• Proven track record of mentoring and developing junior team members, running training programmes, and
• influencing technical direction across teams.