AI Engineer

We are looking for an AI Engineer who is especially interested in Natural Language Processing (NLP), Large Language Models (LLMs) and document intelligence, with working knowledge of Computer Vision (CV). You will work closely with our Tech Lead and product team to take AI capabilities from prototypes into reliable product features used by real customers.

A key focus will be our Tender Management Solution (TMS): helping infrastructure consultancy and other enterprise teams understand complex tender documents, identify bid requirements, assess eligibility, and reuse their company's experience, credentials and knowledge to prepare stronger bids. This is an opportunity to solve challenging document-understanding and information-retrieval problems, not simply build a generic chatbot.

Responsibilities:

• Build NLP/LLM pipelines that process lengthy government and enterprise tender documents, including RFPs, RFQs, eligibility criteria, technical requirements, financial clauses, deadlines and submission checklists.
• Develop reliable information-extraction workflows to identify and structure key tender fields, with validation, confidence indicators and traceability to the source page or text.
• Design and improve Retrieval-Augmented Generation (RAG) systems so users can ask questions across tender documents and company knowledge, with answers grounded in retrieved evidence and citations.
• Work on document ingestion and parsing for PDFs and scanned documents, including OCR, layout-aware extraction, tables, multi-column pages and document chunking strategies.
• Build semantic search, embeddings, reranking and hybrid retrieval approaches to retrieve the most relevant tender clauses, past project credentials, CVs and organisational documents.
• Develop AI-assisted eligibility checking by comparing tender qualification criteria against company credentials, financial thresholds, certifications, project experience and personnel requirements.
• Help build the TMS Company Vault: convert unstructured company documents, project sheets, CVs, credentials and past bid material into a searchable, reusable knowledge base.
• Develop CV and project-experience matching workflows that map personnel qualifications, roles, years of experience and project history to tender requirements, while highlighting gaps for human review.
• Create structured outputs such as tender summaries, compliance matrices, risk flags, bid/no-bid inputs and submission checklists, with clear evidence for each recommendation.
• Evaluate LLM outputs for extraction accuracy, retrieval quality, groundedness and consistency; create test datasets and regression checks to reduce hallucinations and brittle prompt behaviour.
• Experiment with prompt engineering, tool/function calling, model selection and, where justified, fine-tuning or smaller specialised models to balance quality, latency and cost.
• Build safeguards for missing or conflicting information, low-confidence outputs, sensitive company data and human approval of high-impact bid decisions.

Computer Vision:

• Contribute to computer-vision use cases across infrastructure monitoring, solid waste management and operational automation.
• Use OpenCV and detection models such as YOLO to prototype, evaluate or improve object detection and image/video analysis workflows.
• Understand image annotation, data preparation, augmentation, model evaluation and deployment considerations for real-world camera feeds.
• Collaborate with the team to integrate model outputs into production applications and dashboards.

Requirements:

• Strong Python programming skills and practical experience building AI/ML applications.
• Good understanding of NLP fundamentals and hands-on familiarity with LLMs, prompt engineering and/or RAG-based applications.
• Experience with at least one major LLM API or model ecosystem, such as OpenAI, Gemini, Anthropic or open-source Hugging Face models.
• Familiarity with embeddings, vector databases and semantic search; experience with tools such as FAISS, Qdrant, Pinecone, Milvus or pgvector is useful.
• Understanding of document processing concepts such as OCR, text extraction, chunking, metadata, retrieval and structured output validation.
• Working knowledge of ML fundamentals and model evaluation; basic familiarity with computer vision concepts and tools such as OpenCV or YOLO.
• Comfort working with APIs, Git/GitHub, debugging and collaborating with software engineers to integrate AI features into applications.

Good to Have:

• Experience with LangChain, LlamaIndex, Hugging Face Transformers or similar orchestration/model libraries.
• Hands-on work with scanned PDFs, OCR engines, layout-aware document models, table extraction or multilingual documents.
• Experience evaluating RAG systems, building test/evaluation datasets, reranking, hybrid search or observability for LLM applications.
• Exposure to PyTorch or TensorFlow, YOLO, image augmentation or real-time video analytics.
• Experience deploying services to AWS, GCP, Azure or Linux-based environments; familiarity with Docker is an advantage.
• Interest in enterprise workflows, bid/tender processes, infrastructure consulting or document-heavy business operations.

Nice to Have:

• A builder's mindset: you enjoy researching a problem, testing alternatives and delivering a working solution.
• Product thinking: you care about whether the AI output is accurate, explainable and genuinely useful to an end user.
• A quality-first approach: you know that enterprise AI must handle exceptions, cite evidence and signal uncertainty, not just produce fluent answers.
• Curiosity and ownership: you can communicate your approach, document decisions and work closely with the Tech Lead and product team.