AI Platform Engineer

Job Title: AI Platform Engineer

Department: CEFS IT (CRM&S - CRM)

About Business line/Function: CIB

Experience Required: 7+ years in software engineering, AI engineering or related fields (or equivalent proven experience)

Product: AI‑Native CRM Platforms

Position Purpose

Looking for a highly skilled candidate with 7+ years of experience to join our dynamic development team as an AI Platform Engineer. In this role, the candidate will architect and deploy autonomous and semi-autonomous AI agentic systems designed to automate complex CRM+ application features and orchestrate multi-step processes across enterprise data. This is an exceptional opportunity for a candidate to drive innovation in Agentic AI, RAG pipelines, and LLMOps within a high-scale, regulated corporate and institutional banking environment.

Responsibilities

Direct Responsibilities

• Design, develop, and deploy autonomous and semi‑autonomous AI agents that automate CRM workflows (sales assistance, support automation, lead qualification) and orchestrate multi‑step processes across enterprise data. Implement permission‑aware tool adapters, bounded autonomy, guard‑rails, escalation mechanisms and full audit‑logging to satisfy AML and security compliance
• Build production‑grade Retrieval‑Augmented Generation (RAG) pipelines that ingest structured and unstructured data, perform chunking, embedding, hybrid search, semantic retrieval, metadata filtering and re‑ranking using vector databases (pgvector, Qdrant, Pinecone, Weaviate). Create reusable LLM abstraction layers that support multiple model providers and open‑weight models, with model routing, fall‑backs, rate‑limiting, cost‑tracking and token‑consumption optimization (caching, context‑window management)
• Design and implement robust state & memory management for autonomous agents—handle checkpointing, persistent context storage, efficient serialization/retrieval, and scalable memory‑access patterns to support long‑running, multi‑turn interactions while maintaining consistency and compliance.
• Develop AI‑powered CRM capabilities such as customer profiling, lead qualification, meeting preparation, interaction summarization, follow‑up generation and next‑best‑action recommendation. Build natural‑language interfaces that let users query customer information, generate insights and trigger workflows conversationally. Enable agents to retrieve context, update CRM records and execute authorized actions while integrating LLM reasoning with business‑rule engines and existing CRM processes
• Engineer scalable backend services and APIs (Python, FastAPI, async) that expose agent functionality, integrate with CRM systems, databases, email/calendar services, document repositories and external APIs. Implement reliable execution patterns – background jobs, message queues, retries, timeouts, idempotency and fault tolerance – and construct event‑driven architectures with Kafka or RabbitMQ.
  
  

Design and operate AI‑infrastructure and LLMOps pipelines: containerize services (Docker), orchestrate on‑prem or cloud‑native Kubernetes clusters (Helm, Argo CD GitOps), manage CI/CD (GitHub Actions, Jenkins), and implement observability (Prometheus, Grafana, ELK) together with model‑lifecycle tooling (MLflow, experiment tracking, model registry). Optimize latency, throughput, concurrency, resource utilization and inference cost; enforce access controls, tenant isolation, secrets management and audit trails.

• Provide technical leadership across product, engineering and compliance teams: own end‑to‑end architecture, prototype, productionise and maintain AI solutions; develop reusable frameworks and standards for agentic AI; conduct code and architecture reviews; mentor junior engineers; and continuously evaluate emerging agentic‑AI frameworks, models and deployment technologies for product relevance.
  
  

Contributing Responsibilities

• Proficiency in tools (e.g., JIRA, Confluence, MS Visio)
• Familiarity with software development methodologies (Agile, Scrum, Waterfall).
• Familiarity with Oracle databases, Understanding schema, SQL
• Strong analytical, communication, and interpersonal skills
• Ability to work effectively with cross-functional teams and manage multiple priorities.
• Quick to learn and adapt
• Good Team player
• Someone who could have long-term commitment with the team and organization
  
  

Technical & Behavioral Competencies

• Agentic AI & Multi‑Agent Systems – Mastery of LangGraph / LangChain, tool‑calling, function‑calling, planning, task decomposition, state management, checkpointing, retries, human‑in‑the‑loop approval and performance metrics (completion rate, accuracy, latency, cost).
• LLM & RAG Engineering – End‑to‑end RAG pipeline design, context‑engineering, prompt optimization, grounding, hallucination mitigation, token‑window management, caching strategies and multi‑provider model routing.
• AI‑Powered CRM Capability Design – Ability to translate CRM domain expertise into intelligent features (profiling, scoring, summarization, recommendation) and embed LLM‑driven reasoning with rule‑engine and workflow automation.
• Backend & Integration Engineering – Delivery of high‑availability micro‑services (FastAPI/Node.js), event‑driven pipelines (Kafka, RabbitMQ, Avro/Schema Registry), secure API‑gateway configuration (Kong) and robust integration adapters that enforce permission models and idempotent behavior.
• Cloud‑Native & DevOps Practices – Deep experience with Docker, Kubernetes (multi‑namespace, Helm, Argo CD), CI/CD automation, Infrastructure‑as‑Code (Terraform, Pulumi) and observability stacks (Prometheus, Grafana, ELK/Loki).
• Data & Retrieval Engineering – Proficiency with SQL/relational databases, vector‑search technologies, document processing, feature‑store patterns (Feast, Redis), data‑quality governance, schema evolution and caching.
• Machine‑Learning Foundations – Practical knowledge of scikit‑learn, PyTorch, TensorFlow, model training/evaluation, hyper‑parameter tuning and lifecycle management with MLflow.
• Security, Governance & Compliance – Implementation of permission‑aware tool execution, audit logging, model governance and adherence to AML, GDPR and internal risk‑management policies.
• Performance Measurement & Optimization – Definition and monitoring of agent/LLM metrics (accuracy, latency, cost), systematic A/B testing and optimization techniques (semantic caching, prompt caching, context compression).
  
  

Nice to have

• Experience with vLLM, LoRA/QLoRA fine‑tuning, quantization and GPU‑based model serving behind firewalls.
• Hands‑on work with LLM evaluation and tracing platforms such as LangSmith, Langfuse, Phoenix, OpenTelemetry or comparable tools.
• Advanced agentic‑AI patterns: durable execution, Model‑Context Protocol (MCP), persistent agent memory and sophisticated human‑in‑the‑loop workflows.
• Proficiency with common ML libraries (scikit‑learn, PyTorch, TensorFlow) for rapid prototyping and model development.
• Knowledge‑graph expertise (Neo4j, Memgraph, TigerGraph, Amazon Neptune, JanusGraph) for large‑scale network analytics and entity resolution.
• Experience with large‑scale streaming and batch processing frameworks (Kafka, Spark, Flink, Delta Lake) and data‑pipeline orchestration tools (dbt, Airflow).
• Familiarity with multi‑tenant SaaS architecture, OAuth2/OpenID Connect, SSO and enterprise identity‑management solutions.
• Background in recommendation, ranking, lead‑scoring, customer segmentation, forecasting or churn‑prediction models for CRM intelligence.
• Infrastructure‑automation expertise (Terraform, Pulumi, GitOps, Helm, Argo CD) and sandbox quota management for GPU nodes.
  
  

Specific Qualifications: NA

Technical Skills

Skills Referential (Required knowledge, skills and abilities)

• Core Programming & Software Engineering – Python, FastAPI/Flask, asynchronous development, REST/GraphQL APIs, SQL/relational databases, Git version control, automated testing, CI/CD best practices.
• Agentic AI & LLM Engineering – LangGraph/LangChain, tool/function calling, state & memory management, planning, task decomposition, multi‑agent orchestration, RAG pipelines, vector‑DB integration, prompt engineering, cost & rate‑limit controls, model routing.
• Backend & Integration Engineering – High‑availability micro‑services (FastAPI/Node.js), event‑driven pipelines (Kafka, RabbitMQ, Avro/Schema Registry), API‑gateway configuration (Kong), rule‑engine integration (Drools or custom), secure credential handling, idempotent design.
• Cloud‑Native & DevOps – Docker, Kubernetes (multi‑namespace, Helm, Argo CD GitOps), CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI), IaC (Terraform, Pulumi), observability (Prometheus, Grafana, ELK/Loki), secrets management, tenant isolation.
• Data & Retrieval Engineering – Structured and semi‑structured data handling, document ingestion & processing, embedding generation, vector‑search (pgvector, Qdrant, Pinecone, Weaviate), feature‑store patterns (Feast, Redis), data lineage, quality and governance.
• Machine‑Learning Foundations – Scikit‑learn, PyTorch, TensorFlow, model training, evaluation, hyper‑parameter optimization, MLflow experiment tracking, model registry and artifact versioning.
• Collaboration & Problem‑Solving – Cross‑functional communication, stakeholder alignment, comprehensive documentation, mentorship, systematic troubleshooting and adaptability to regulated environments.
  
  

Behavioral Skills

• Collaboration & Communication – Proven ability to work in globally distributed, cross‑functional teams; translate complex AI concepts into actionable specifications; produce clear technical documentation (OpenAPI/Swagger, design specs); mentor junior engineers.
• Problem‑Solving Mindset & Adaptability – Systematic, data‑driven approach to ambiguous CRM/financial‑crime typologies; rapid learning of new domains, regulations and technologies while maintaining high‑quality, secure solutions.
• Professional Background – bachelor's or master's degree in computer science, Engineering, AI or related discipline (or equivalent experience); ≥ 7 years of demonstrable experience delivering production‑grade software and AI systems; documented ownership of end‑to‑end AI product features.
  
  

Education Level: Bachelor's degree or equivalent

Location: Mumbai

About BNP Paribas Group

BNP Paribas is the European Union's leading bank and key player in international banking. It operates in 65 countries and has nearly 185,000 employees, including more than 145,000 in Europe. The Group has key positions in its three main fields of activity: Commercial, Personal Banking & Services for the Group's commercial & personal banking and several specialized businesses including BNP Paribas Personal Finance and Arval; Investment & Protection Services for savings, investment, and protection solutions; and Corporate & Institutional Banking, focused on corporate and institutional clients. Based on its strong diversified and integrated model, the Group helps all its clients (individuals, community associations, entrepreneurs, SMEs, corporate and institutional clients) to realize their projects through solutions spanning financing, investment, savings and insurance. In Europe, BNP Paribas has four domestic markets: Belgium, France, Italy, and Luxembourg. The Group is rolling out its integrated commercial & personal banking model across several Mediterranean countries, Turkey, and Eastern Europe. As a key player in international banking, the Group has leading platforms and business lines in Europe, a strong presence in the Americas as well as a solid and fast-growing business in Asia-Pacific. BNP Paribas has implemented a Corporate Social Responsibility approach in all its activities, enabling it to contribute to the construction of a sustainable future, while ensuring the Group's performance and stability.

About BNP Paribas India Solutions

Established in 2005, BNP Paribas India Solutions is a wholly owned subsidiary of BNP Paribas SA, European Union's leading bank with an international reach. With delivery centers located in Bengaluru, Chennai and Mumbai, we are a 24x7 global delivery center. India Solutions services three business lines: Corporate and Institutional Banking, Investment Solutions and Retail Banking for BNP Paribas across the Group. Driving innovation and growth, we are harnessing the potential of over 10000 employees, to provide support and develop best-in-class solutions.

Commitment to Diversity and Inclusion

At BNP Paribas, we passionately embrace diversity and are committed to fostering an inclusive workplace where all employees are valued, respected and can bring their authentic selves to work. We prohibit Discrimination and Harassment of any kind and our policies promote equal employment opportunity for all employees and applicants, irrespective of, but not limited to their gender, gender identity, sex, sexual orientation, ethnicity, race, color, national origin, age, religion, social status, mental or physical disabilities, veteran status etc. As a global Bank, we truly believe that inclusion and diversity of our teams is key to our success in serving our clients and the communities we operate in. As a global Bank, we truly believe that inclusion and diversity of our teams is key to our success in serving our clients and the communities we operate in.