contract IT Consultant #IAC

Note: Successful candidates need to be comfortable with working on weekends and shift as and when required.

Responsibilities

Translate business requirements into AI architectures, implementation plans and measurable evaluation criteria. Develop reusable agents and RAG components for market intelligence, client engagement, workflow automation and enterprise knowledge management.

Build predictive models for client propensity, classification, ranking, behavioural analytics, recommendations and next-best-action use cases. Perform exploratory data analysis, feature engineering, model training, hyperparameter tuning and evaluation.

Establish suitable baselines and validation strategies, prevent data leakage and assess model generalisation. Build Python-based AI services and REST APIs, integrating structured and unstructured enterprise data. Deploy and support applications using Docker, Kubernetes and Red Hat OpenShift/OCP.

Implement CI/CD, automated testing, model/application monitoring and operational documentation. Investigate production failures across retrieval, agent execution, model inference, APIs and infrastructure. Optimise accuracy, relevance, latency, reliability and operating cost.

Collaborate with business, product, data, engineering, infrastructure and security teams.

Any other ad-hoc duties as assigned by supervisors Mandatory Generative AI Requirements Hands-on experience in all five areas below is required: RAG: Implementing document ingestion, parsing, chunking, embeddings, retrieval, reranking and grounded response generation.

Vector Search: Implementing vector indexing, similarity search, metadata filtering and retrieval-quality evaluation. LLM Applications: Building applications integrated with enterprise data sources and APIs, including output validation, error handling and monitoring.

Dify: Building LLM applications or agent workflows, configuring knowledge bases, integrating tools and publishing applications through APIs. Agentic AI Frameworks: Imp