Position Details: Senior AI Engineer

Description:

Position : Senior AI Engineer.

About This Role

Join our Engineering team as a Senior AI Engineer leading the design and delivery of a

strategic, regulated-grade AI platform initiative.

The work spans conversational AI for customer

engagement, customer analytics and segmentation, and AI-driven workforce solutions.

You will

own the end-to-end AI architecture across Large Language Models (LLMs), Retrieval

Augmented Generation (RAG), agentic workflows, and classical machine learning.

You will set

technical direction, enforce responsible-AI standards, and mentor a team of mid-level AI

engineers delivering production use cases in a tightly regulated fintech environment.

Key Responsibilities

• AI Architecture & Delivery: Design and deliver multi-use-case AI solutions spanning

conversational AI, ML-based customer segmentation, and agentic workflow automation,

ensuring low-latency inference, secure cloud deployment, and regulatory alignment

• LLM & RAG Systems: Architect production-grade Retrieval-Augmented Generation

pipelines with vector databases, embeddings strategy, grounded responses, and

prompt/response guardrails for compliance-sensitive conversational AI

• Agentic AI: Build multi-step agentic workflows with tool use, memory, and human-in

the-loop checkpoints for automated outreach, decision support, and Next-Best-Action

recommendations

• Multilingual Model Engineering: Lead fine-tuning and evaluation of small/large

language models across regional and South-Asian languages (including Arabic, Nepali,

Bengali, Malayalam) to serve a diverse customer base

• Classical ML & Propensity Modeling: Oversee Customer Lifetime Value (CLV), RFM,

and real-time propensity scoring models backed by feature stores and champion

challenger frameworks

• Responsible AI & Safety: Implement bias detection, explainability (SHAP, LIME),

Personally Identifiable Information (PII) masking, red-team testing, and automated

guardrails; own compliance alignment with applicable data protection and financial

services regulations

• Technical Leadership: Mentor mid-level AI engineers, lead design reviews, set coding

and MLOps standards, and accelerate delivery against a compressed pilot timeline

• Stakeholder Collaboration: Partner with product managers, data engineers, risk,

compliance, and business stakeholders to translate business requirements into

explainable, audit-ready AI solutions

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science,

or a related field

• Minimum of 7 years of professional experience in AI/ML engineering, with at least 2

years on production LLM or GenAI systems

• Expert-level Python; strong software engineering fundamentals and API design
• Deep experience with LLM frameworks (LangChain, LlamaIndex, or Semantic Kernel)

and RAG architectures

• Hands-on experience with vector databases (Pinecone, Weaviate, Milvus, or pgvector)

and embedding models

• Proven experience designing agentic AI workflows with tool calling, planning, and

evaluation loops

• Strong classical ML expertise: scikit-learn, XGBoost, PyTorch or TensorFlow, feature

engineering, model evaluation

• Experience fine-tuning transformer models (LoRA, QLoRA, instruction tuning) and

evaluating multilingual LLMs/SLMs

• MLOps: model serving, monitoring, drift detection, CI/CD for ML (MLflow, Kubeflow,

SageMaker, or equivalent)

• Responsible AI: bias/fairness auditing, explainability techniques, PII handling, and

guardrail frameworks (Guardrails AI, NeMo Guardrails)

• Experience leading and mentoring AI/ML engineering teams

Preferred Qualifications

• Experience delivering AI in banking, fintech, or other regulated industries
• Working knowledge of Arabic Natural Language Processing (NLP) and South-Asian

language models

• Familiarity with on-premise, private-cloud, or sovereign-cloud deployment of AI

workloads

• Experience with feature stores (Feast, Tecton) and real-time scoring infrastructure
• Understanding of financial-services data protection regulations (e.g., PDPA, GDPR, or

equivalent)

• Contributions to open-source AI projects or published research

Perform an action: