Technical Lead

Job Summary

To be responsible for managing technology in projects and providing technical guidance / solutions for work completion

Key Responsibilities

1. To be responsible for providing technical guidance / solutions ;define, advocate, and implement best practices and coding standards for the team.

2. To develop and guide the team members in enhancing their technical capabilities and increasing productivity

3. To ensure process compliance in the assigned module| and participate in technical discussions/review as a technical consultant for feasibility study (technical alternatives, best packages, supporting architecture best practices, technical risks, breakdown into components, estimations).

4. To prepare and submit status reports for minimizing exposure and risks on the project or closure of escalations.

Skill Requirements

SENIOR AI / ML ENGINEER Role Summary Designs, develops, deploys, and optimises Artificial Intelligence (AI) and Machine Learning (ML) solutions within the Software Engineering Centre of Excellence (CoE). Works closely with software engineering squads, data teams, architects, DevOps, and business stakeholders to build scalable AI-enabled products, intelligent automation capabilities, predictive models, and advanced analytics solutions that support business and customer outcomes. Responsibilities Design, develop, train, test, and deploy machine learning and AI models for enterprise and customer-facing solutions Collaborate with software engineering squads, data engineers, architects, and product owners to integrate AI capabilities into digital products and platforms Build scalable AI/ML pipelines and support operationalisation of models within production environments Develop and optimise models for predictive analytics, automation, recommendation engines, NLP, GenAI, and intelligent decisioning solutions Work with large structured and unstructured datasets to support AI solution development Support AI platform engineering, model governance, monitoring, and continuous improvement practices Implement MLOps and DevOps best practices for model deployment, testing, monitoring, and lifecycle management Evaluate and recommend AI frameworks, tools, accelerators, and emerging technologies Support development of reusable AI engineering assets, accelerators, and standards within the CoE Collaborate with security, governance, and compliance teams to ensure responsible AI practices and data governance compliance Support technical solution design, estimation, and implementation planning for AI-enabled initiatives Provide technical leadership and mentorship to engineering teams on AI/ML engineering practices Contribute to continuous improvement, innovation initiatives, and AI capability development within the Software Engineering CoE Qualification & Experience Bachelor’s degree (BA/BS) in Computer Science, Data Science, Artificial Intelligence, Mathematics, Engineering, or related field preferred At least 8–10 years of experience in software engineering, AI, machine learning, or data engineering environments At least 5 years of hands-on experience developing and deploying AI/ML solutions in enterprise environments Experience with machine learning frameworks and tools such as Tensor Flow, Py Torch, Scikit-learn, MLflow, Lang Chain, or similar technologies Experience with cloud AI platforms and services such as AWS, Azure, or Google Cloud Experience with MLOps, CI/CD pipelines, containerisation, and cloud-native engineering practices Strong understanding of software engineering principles, APIs, microservices, and scalable system design Experience working

Other Requirements

SENIOR AI / ML ENGINEER Role Summary Designs, develops, deploys, and optimises Artificial Intelligence (AI) and Machine Learning (ML) solutions within the Software Engineering Centre of Excellence (CoE). Works closely with software engineering squads, data teams, architects, DevOps, and business stakeholders to build scalable AI-enabled products, intelligent automation capabilities, predictive models, and advanced analytics solutions that support business and customer outcomes. Responsibilities Design, develop, train, test, and deploy machine learning and AI models for enterprise and customer-facing solutions Collaborate with software engineering squads, data engineers, architects, and product owners to integrate AI capabilities into digital products and platforms Build scalable AI/ML pipelines and support operationalisation of models within production environments Develop and optimise models for predictive analytics, automation, recommendation engines, NLP, GenAI, and intelligent decisioning solutions Work with large structured and unstructured datasets to support AI solution development Support AI platform engineering, model governance, monitoring, and continuous improvement practices Implement MLOps and DevOps best practices for model deployment, testing, monitoring, and lifecycle management Evaluate and recommend AI frameworks, tools, accelerators, and emerging technologies Support development of reusable AI engineering assets, accelerators, and standards within the CoE Collaborate with security, governance, and compliance teams to ensure responsible AI practices and data governance compliance Support technical solution design, estimation, and implementation planning for AI-enabled initiatives Provide technical leadership and mentorship to engineering teams on AI/ML engineering practices Contribute to continuous improvement, innovation initiatives, and AI capability development within the Software Engineering CoE Qualification & Experience Bachelor’s degree (BA/BS) in Computer Science, Data Science, Artificial Intelligence, Mathematics, Engineering, or related field preferred At least 8–10 years of experience in software engineering, AI, machine learning, or data engineering environments At least 5 years of hands-on experience developing and deploying AI/ML solutions in enterprise environments Experience with machine learning frameworks and tools such as Tensor Flow, Py Torch, Scikit-learn, MLflow, Lang Chain, or similar technologies Experience with cloud AI platforms and services such as AWS, Azure, or Google Cloud Experience with MLOps, CI/CD pipelines, containerisation, and cloud-native engineering practices Strong understanding of software engineering principles, APIs, microservices, and scalable system design Experience working