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. Valuable 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.

📌 AI ML Gen AI Engineer (Banking Compliance) (Gurugram)
🏢 Horizon Industries International
📍 Gurugram