Senior ML & AI Engineer

About the role

We are looking for a Senior ML & AI Engineer to lead the design and engineering of enterprise AI/ML, NLP, Generative BI, GenAI and Agentic AI solutions for regulated banking environments. The role involves building reusable frameworks, operationalising models and ensuring responsible AI practices across the organisation.

Key responsibilities

• Lead the design and engineering of AI/ML, NLP, Generative BI, GenAI and Agentic AI solutions for banking.
• Build reusable frameworks for Retrieval‑Augmented Generation (RAG), LLM applications, agent workflows, code/test generation and advanced analytics.
• Define and implement feature engineering, model and agent evaluation, prompt testing, human‑in‑the‑loop review and acceptance criteria.
• Operationalise ML models and AI agents using MLOps pipelines, MLflow, CI/CD, model registries, monitoring, observability and rollback controls.
• Integrate enterprise LLMs with governed data, semantic layers, metadata, vector databases, BI platforms, APIs and cloud AI services.
• Lead technical reviews, production‑readiness assessments, performance optimisation, responsible AI, security, governance and mentor AI/ML engineering teams.

Required profile

• Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Data Science or related field.
• 10+ years of experience in software engineering, analytics, AI/ML or data science with strong technical leadership.
• Hands‑on expertise in Python, Spark, Scala/Java, TensorFlow, PyTorch, scikit‑learn, MLflow and modern LLM/Agent frameworks.
• Proven experience with RAG, embeddings, vector search, LLMs, Agentic AI, prompt engineering, model evaluation, monitoring and drift detection.
• Strong knowledge of Hadoop/Cloudera, cloud AI services, Kubernetes, APIs, MLOps, CI/CD, observability, model governance and responsible AI.
• Banking domain experience (AML, risk, payments, finance or customer analytics) and strong stakeholder management.

Required skills

• Python
• Spark
• Scala
• Java
• TensorFlow
• PyTorch
• scikit‑learn
• MLflow
• Hadoop
• Cloudera
• Kubernetes
• CI/CD
• MLOps
• APIs
• Vector databases
• LLM frameworks (RAG, embeddings, prompt engineering)