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.