[BD] Senior Artificial Inteligence Developer

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AI Global Product & Service Transformation | Serving 500,000+ users worldwide Are you ready to build a native‑AI product at global scale? This isn't a "POC" or a short‑term experiment. We are building a long‑term, strategic Agentic AI architecture to transform an enterprise workflow platform used by 500k+ users globally into an AI‑first product -one that can understand requirements, generate workflow applications (and code), validate quality, and continuously improve accuracy and user outcomes . We've already completed the foundation for two AI agents in our AI architecture. Now we're scaling from "agents exist" to agents that are accurate, reliable, secure, and production‑ready at enterprise scale . If you want your work to matter-and want a rare chance to ship AI into real operations across a major corporation-this role is for you. 🚀 Why this role is a career‑defining move 1) Build the future-not just models. You will help create Agentic AI that can generate workflow applications from business requirements or legacy source code , including deployment artifacts and modernization outputs. 2) Massive real‑world impact. Your engineering decisions will affect hundreds of thousands of users -with measurable outcomes: cycle time, quality, compliance, and platform cost reduction. 3) Hard problems, serious ownership. This role demands more than AI knowledge. You'll need strong full‑stack engineering maturity to build robust systems around LLMs: orchestration, evaluation, observability, security, and scalable delivery. 4) Long‑term mission with real investment. This is a multi‑year transformation with committed funding and roadmap. We're looking for an engineer who wants deep ownership , long‑term growth, and meaningful achievements-not quick wins. 🎯 Your mission Help us evolve our AI‑Driven Workflow Platform into a native AI Product & Service by building an Agentic AI architecture that can: Turn legacy knowledge (data, logs, procedures, docs) into usable intelligence Generate workflow apps (and code) from requirements or existing legacy assets Improve accuracy through evaluation, feedback loops, and continuous learning Operate safely at enterprise scale with governance, security, and reliability 🧩 What you will do (Responsibilities) Turn Legacy into Leverage Deep‑dive into legacy environments-SQL procedures, logs, documentation, and siloed datasets Design pipelines that extract structured intelligence from historical systems Convert fragmented data into high ‑ quality, AI ‑ ready datasets Build AI at the Core of the Product Design, develop, and deploy production‑grade AI capabilities for the AI‑Driven Workflow Platform Enable intelligent workflow routing, natural‑language interfaces (LLMs), predictive insights, and anomaly detection Engineer Signals That Matter Lead feature engineering focused on workflow optimization Identify bottlenecks, predict failures, and surface insights that directly improve operational efficiency Own Global‑Scale AI Systems Drive MLOps strategy across regions Own the full AI lifecycle: training, evaluation, deployment, monitoring, and optimization Optimize for accuracy, latency, cost, scalability, and maintainability Modernize with Generative AI Apply GenAI and LLMs to reverse‑engineer and modernize legacy codebases Transform outdated logic into clean, documented, cloud‑native services Lead and Influence Mentor engineers and raise AI engineering standards Work closely with Product teams to translate complex processes into intuitive, AI‑powered user experiences 🛠️ Tech areas you'll likely work with (We're flexible on exact tools if you can deliver outcomes.) LLMs / GenAI: prompt engineering, RAG, fine‑tuning (where needed), structured generation, tool‑calling Agent frameworks: LangChain/LangGraph or equivalents Backend: Python (must), microservices, REST/gRPC, event‑driven patterns Data/Knowledge: ETL/ELT, vector DBs, graph DBs, search, indexing, document pipelines MLOps/LLMOps: MLflow/Airflow or equivalents, CI/CD, eval pipelines Infra: Docker/Kubernetes, cloud (Azure/AWS/GCP), monitoring/telemetry Must‑Have 7+ years in software or data engineering, including 4+ years deploying AI/ML systems in large‑scale production Expert‑level Python Hands‑on experience with modern LLM workflows : fine‑tuning, RAG, evaluation, agent orchestration (LangChain, LangGraph, or equivalent) Strong full‑stack engineering background with end‑to‑end system ownership Deep experience in data engineering : ETL pipelines, NoSQL / Graph databases, large‑scale processing (e.g., Spark) High tolerance for messy enterprise data and complex legacy environments Practical experience with MLOps (MLflow, Airflow), containers (Docker, Kubernetes), and cloud platforms (AWS, Azure, or GCP) Nice‑to‑Have Experience modernizing legacy systems or extracting intelligence from older architectures Background in Low‑Code / No‑Code platforms or business process automation Knowledge of performance and cost optimization (model com

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