Description

Key Responsibilities

• Translate banking business problems into practical AI/ML solutions.
• Determine when to use traditional ML versus LLM/GenAI approaches.
• Design and implement end-to-end AI systems, including data pipelines, feature engineering, model integration, and APIs.
• Build and enhance RAG and agentic AI architectures using LangChain, LangGraph, or equivalent.
• Develop production-ready AI services with error handling, fallbacks, guardrails, logging, monitoring, and tracing.
• Implement AI safety controls including input validation, prompt-injection mitigation, configurable policies, and kill-switch mechanisms.
• Optimize AI applications for performance, latency, and cost.
• Convert PoCs into production-ready systems through testing, refactoring, and deployment.
• Work with SQL, NoSQL, and vector databases.
• Build and deploy traditional ML models for classification, regression, clustering, anomaly detection, and recommendation use cases.
• Build and integrate LLM solutions using OpenAI, Claude, Gemini, Llama, or equivalent models.
• Apply prompt engineering and LLM evaluation techniques.
• Develop tool-based, agentic, and multi-agent workflows.
• Collaborate with cloud, platform, infrastructure, product, and business teams.
• Follow strong software engineering, version control, documentation, and CI/CD practices.

Required Skills

Software Engineering

• 3–5 years of software engineering experience.
• At least 2 years of ML/AI engineering experience.
• Strong Python development skills.
• Java or Node.js is a plus.
• Understanding of distributed systems and data pipelines.
• Docker and basic Kubernetes knowledge.

Machine Learning & GenAI

• Hands-on experience building and deploying traditional ML models.
• Experience with scikit-learn, PyTorch, or TensorFlow.
• Production experience delivering 1–2 LLM/GenAI applications.
• RAG and vector search experience.
• Prompt engineering and LLM evaluation.
• Experience with LangChain, LangGraph, AutoGen, or equivalent.

Cloud & DevOps

• Hands-on experience with AWS, Azure, or GCP.
• CI/CD and deployment automation.
• Model and code versioning.
• Configuration management.

Data & Databases

• SQL and NoSQL database experience.
• Vector databases such as ChromaDB, Pinecone, pgvector, or equivalent.
• Data ingestion and feature engineering pipelines.

Production & Observability

• Logging, monitoring, and alerting for production AI systems.
• Understanding of resilience patterns such as:
  • Rate limiting
  • Failover
  • Circuit breakers

Banking / Compliance

• Understanding of explainability, fairness, and auditability in financial AI.
• Experience with AI guardrails and safety controls.
• Awareness of responsible AI and AI governance.
• Understanding of data privacy, PII handling, and secure engineering practices.
• GDPR, RBI, or equivalent regulatory awareness is beneficial.

Nice to Have

• Banking, financial services, fintech, or payments experience.
• AI governance or model risk management experience.
• Neo4j or other graph database experience.
• Open-source AI/ML contributions or GenAI publications.
• MLOps experience with MLflow or Weights & Biases.
• Multi-agent architecture experience.

Education

Bachelor's or Master's degree in:

• Computer Science
• Data Science
• Artificial Intelligence / Machine Learning
• Engineering
• Related field

Key Skills

Education

Any Gradute