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.