Senior AI Engineer / AI Technical Lead at Green Com Enterprise Solutions Ltd

The Opportunity

• Green Com Enterprise Solutions Limited is establishing an Artificial Intelligence & Emerging Technologies capability to develop secure, practical and enterprise-grade AI solutions for government, regulatory and enterprise organizations.
• We are seeking a highly hands-on Senior AI Engineer / AI Technical Lead who will play a foundational role in building this capability.
• This is not purely a management position. The successful candidate will be expected to architect solutions, write code, build prototypes, deploy AI applications, mentor engineers and help transform business requirements into production-ready AI systems.

Key Responsibilities

The successful candidate will:

• Lead the technical establishment of Green Com’s AI capability.
• Design and develop production-grade AI and machine-learning solutions.
• Build applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings and vector databases.
• Design AI solutions for document processing, knowledge management, analytics, workflow automation and enterprise systems.
• Integrate AI capabilities with existing enterprise applications, APIs, databases and identity-management systems.
• Design secure AI architectures for cloud, hybrid and on-premise environments.
• Establish standards for AI security, model evaluation, testing, monitoring and responsible AI.
• Evaluate commercial and open-source AI models and recommend appropriate technologies for different use cases.
• Develop AI proof-of-concepts and convert successful prototypes into production solutions.
• Mentor Green com software developers and help develop internal AI engineering capability.
• Participate in technical presentations, demonstrations, proposals and AI consultancy engagements.
• Contribute technical input to AI tenders and proposals.
• Research emerging AI technologies and advise management on commercially relevant opportunities.

Technical Competencies

Candidates should demonstrate strong practical knowledge of:

• Python and modern software-engineering practices
• Machine learning and deep-learning fundamentals
• Large Language Models
• Retrieval-Augmented Generation (RAG)
• Prompt and context engineering
• Embeddings and vector search
• AI agents and tool/function calling
• Model and RAG evaluation
• REST APIs and enterprise application integration
• SQL and data engineering
• Docker/containerization
• Git and CI/CD
• Cloud AI platforms, preferably Microsoft Azure
• Authentication, authorization and enterprise security principles
• AI privacy, security, guardrails and responsible AI
• Classical machine-learning techniques (e.g. gradient boosting, classification/regression), not only deep learning and LLMs
• Evaluation and observability tooling for AI systems (e.g. RAGAS, LangSmith, Azure AI Foundry evaluation)
• Data engineering practices, including ETL/pipelines and data validation
• AI-specific security practices, including prompt-injection defense, PII redaction, output filtering and audit logging
• Experience with technologies such as Azure AI services, Azure OpenAI/OpenAI APIs, Hugging Face, PyTorch or TensorFlow, LangChain/LlamaIndex or equivalent frameworks, PostgreSQL/pgvector, Qdrant, Pinecone, Weaviate or similar technologies will be advantageous.
• Candidates are not expected to have worked with every technology listed above. We are more interested in strong fundamentals, demonstrated engineering ability and the ability to select appropriate technologies for a problem.

Enterprise Experience

Experience in one or more of the following will be advantageous:

• Government or public-sector systems
• Enterprise ERP/workflow systems
• Microsoft technology environments
• Document and records-management systems
• Business-process automation
• Data analytics
• Regulatory systems
• Financial/payment systems
• On-premise or hybrid enterprise deployments

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence or a related technical field.
• Strong professional software-development or data-engineering experience.
• 5+ years of professional software-engineering experience, including demonstrable experience building AI/ML applications.
• Experience mentoring or leading engineering teams, with the ability to guide technical direction and grow junior engineers.
• Relevant Microsoft, Azure, AI or cloud certifications will be an added advantage but are not mandatory.

What We Will Assess

• Shortlisted candidates should expect a practical AI engineering assessment and technical presentation.
• Green com values demonstrated ability above knowledge of AI terminology. Candidates should be prepared to explain and defend the architecture, security, scalability and technical decisions behind solutions they have built.

The Person We Are Looking For

We are particularly interested in someone who:

• Enjoys building, not merely managing.
• Can move comfortably from experimentation to production engineering.
• Understands that enterprise AI requires security, governance and reliability.
• Can explain complex AI concepts to non-technical stakeholders.
• Can mentor other engineers.
• Is curious and continuously experiments with emerging technologies.
• Can challenge inappropriate uses of AI rather than applying AI to every problem.
• Wants to help build an AI engineering capability from the ground up.