Key Responsibilities

Architecture & Solution Leadership

Lead the design of enterprise-grade GenAI and agentic architectures (single-agent, multi-agent, tool-driven systems).
Define reference architectures, reusable frameworks, and best practices for LLM applications across the organisation.
Architect and oversee implementation of end-to-end RAG pipelines: - Data ingestion chunking embeddings vector search orchestration response synthesis.
Drive scalability, reliability, cost optimisation, and performance across GenAI platforms.

Agentic & LLM Engineering (Hands-on + Oversight)

Provide technical leadership in prompt engineering, prompt orchestration, and agent workflows (LangChain, LangGraph, etc.).
Guide teams on tool-calling, function-calling, memory handling, and multi-agent system design.
Lead efforts in hallucination reduction, guardrails, safety mechanisms, and output evaluation frameworks.

Platform & Engineering Excellence

Architect production-grade APIs and services (FastAPI/Flask/enterprise microservices) for LLM solutions.
Define MLOps / LLMOps pipelines including CI/CD, monitoring, observability, and evaluation.
Partner with Data Engineering teams to ensure: - Data quality, lineage, governance, and compliance
Seamless integration with enterprise data platforms

Organisation-Level Responsibilities (Critical)

Capability Building & CoE Development

Build and scale GenAI / Agentic AI Centre of Excellence (CoE).
Define standardised frameworks, accelerators, and reusable components to improve delivery velocity.
Drive organisation-wide adoption of GenAI best practices and tooling standards.

Strategic & Stakeholder Leadership

Engage with CXOs, business stakeholders, and clients to translate business problems into AI-led solutions.
Lead solutioning, pre-sales, RFP responses, and client workshops for GenAI opportunities.
Influence AI strategy, roadmap, and investment decisions at organisational level.

Governance, Risk & Compliance

Establish enterprise governance frameworks for GenAI: - Responsible AI, security, privacy, ethical usage, and compliance
Define policies for: - Data access, redaction, model usage, auditability, and explainability

Mentorship & Team Leadership

Experience

Experience & Must-Have Skills

LLM / GenAI & Agentic Engineering

Strong hands-on experience with: - LLMs (Claude, OpenAI, etc.)
RAG pipelines and retrieval optimisation
GPT + Agentic AI implementation experience
Experience with: - LangChain, LangGraph, or similar frameworks
Agent orchestration and tool-calling architectures
Deep understanding of: - LLM limitations, evaluation, and optimisation strategies

Core Engineering

Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
Deep data analysis experience and handling large volume of data
Fabric/Azure Databricks/Snowflake data engineering integration skills
Good exposure to: - Cloud platforms (Azure/AWS/GCP)
SQL
Containers, CI/CD, monitoring

Data / AI Foundations (Mandatory)

Prior Experience In One Or More

Analytics engineering / data products

Good-to-Have / Preferred

Principal Architect AI Data Engineer