AI ML Gen AI Engineer (Banking Compliance)

Key Responsibilities

AI Solution Design & Engineering

Partner with product and business teams to translate banking problems (fraud, credit risk,

customer operations, compliance) into practical AI solutions.

Determine when to apply traditional ML versus LLM/GenAI approaches, evaluating trade-offs

across accuracy, latency, cost, and regulatory constraints.

Design and implement end-to-end AI systems including data pipelines, feature engineering,

model integration, and API-based services.

Build and evolve agentic and RAG (Retrieval-Augmented Generation) architectures using

frameworks such as LangChain or LangGraph.

Production Engineering & Delivery

Build production-ready AI services with robust error handling, fallback mechanisms, guardrails,

and observability (logging, metrics, tracing).

Implement AI safety controls including input validation, prompt injection mitigation,

configurable policies, and kill-switch mechanisms.

Optimise AI systems for performance, latency, and cost — particularly important for high

volume banking workloads.

Transition PoCs and prototypes into hardened production systems through refactoring, testing,

and rigorous deployment practices.

Work with SQL, NoSQL, and vector databases (e.g., PostgreSQL, MongoDB, ChromaDB) to

support data-intensive AI applications.

ML & Generative AI

Apply supervised and unsupervised ML techniques to banking use cases such as classification,

anomaly detection, and recommendation.

Build and integrate LLM-based solutions using models such as OpenAI, Claude, Gemini, Llama, or

equivalent.

Apply prompt engineering, evaluation techniques, and iterative optimisation to improve GenAI

output quality.

Develop tool-based and agentic workflows, including multi-agent systems for complex, multi

step banking processes.

Collaboration & Communication

Collaborate with platform, cloud, and infrastructure teams to ensure reliable deployment and

operations.

Clearly articulate trade-offs (ML vs. LLM, build vs. buy, speed vs. robustness) to both technical

and non-technical stakeholders.

Uphold strong software engineering practices: code quality, documentation, version control, and

CI/CD discipline.

Stay current with advances in GenAI, agentic AI, and MLOps — bringing relevant innovations to

the team.

Required Skills & Experience

Software Engineering

3–5 years of software engineering experience, including at least 2 years in ML/AI engineering

roles.

Strong Python development skills; familiarity with Java or Node.js is a plus.

Solid understanding of distributed systems and data pipeline design.

Containerization experience with Docker; basic Kubernetes knowledge.

AI / Machine Learning

Hands-on experience building and deploying traditional ML models (classification, regression,

clustering, anomaly detection).

Proficiency with ML frameworks: scikit-learn, PyTorch, or TensorFlow.

Real-world experience delivering at least 1–2 LLM or GenAI applications into production.

Familiarity with RAG architectures and vector search.

Working knowledge of prompt engineering and LLM evaluation techniques.

Experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, or equivalent).

Cloud & DevOps

Hands-on experience with at least one major cloud platform: AWS, Azure, or GCP.

Familiarity with CI/CD pipelines and deployment automation.

Understanding of model versioning, code versioning, and configuration management.

Data & Databases

Experience working with SQL databases and NoSQL stores.

Familiarity with vector databases (ChromaDB, Pinecone, pgvector, or equivalent) for embedding

based search.

Ability to build and maintain data ingestion and feature engineering pipelines.

Observability & Production Readiness

Experience implementing logging, monitoring, and alerting for production AI systems.

Familiarity with resilience patterns: rate limiting, failover, circuit breakers.

Banking & Compliance Context

Banking is a regulated environment. While deep compliance expertise is not required at this level,

you should be:

Aware of the importance of explainability, fairness, and auditability in AI models used for

financial decisions (credit, fraud, risk scoring).

Comfortable implementing AI guardrails and safety controls to meet risk, compliance, and audit

requirements.

Willing to work within and learn the organization's AI governance and responsible AI

frameworks.

Mindful of data privacy, PII handling, and secure engineering practices — especially under GDPR,

RBI, or equivalent regulatory regimes.

Good to Have

Prior experience in banking, financial services, fintech, or payments.

Exposure to AI governance, model risk management, or responsible AI frameworks.

Experience with graph databases (e.g., Neo4j) for fraud network or knowledge graph use cases.

Contributions to open-source AI/ML projects or published work in GenAI.

Experience with MLOps tooling: model monitoring, retraining pipelines, experiment tracking

(MLflow, Weights & Biases).

Familiarity with multi-agent architectures for complex workflow automation.