Senior AI Engineer

As a Senior AI Engineer, you will drive the development of our core AI capabilities. Your role is highly strategic: you will architect a scalable, internal AI framework that combines Predictive AI (forecasting, behavioral analytics) and Agentic AI (autonomous multi-agent workflows), while simultaneously adapting and deploying these capabilities to solve complex problems for our enterprise clients. You will bridge the gap between long-term product engineering and high-impact client delivery.

Core Responsibilities

• Core Product & Client Delivery: Architect a modular internal AI framework while actively adapting it to deliver high-performance, domain-agnostic solutions for various clients.
  
• Build Agentic Frameworks: Design and deploy autonomous multi-agent systems capable of multi-step reasoning, tool-use orchestration, and cross-domain automation.
  
• Develop Predictive Pipelines: Train and productionize predictive models for forecasting, anomaly detection, and risk assessment across diverse datasets.
  
• Scalable Production & MLOps: Containerize models into scalable microservices and establish robust MLOps pipelines to monitor both client-facing deployments and internal systems.
  
• Client Consultation & Integration: Collaborate with client technical teams to understand their infrastructure, integrate AI components smoothly, and define clear APIs.
  
• Guardrails & Evaluation: Implement testing frameworks to measure predictive accuracy, evaluate agent safety, and optimize token/compute costs for both the company and clients.
  

Requirements

• Programming: Expert-level Python (writing clean, highly modular, asynchronous, and test-driven production code).
  
• Agentic Ecosystem: Deep experience with multi-agent orchestration tools such as LangGraph, CrewAI, or AutoGen.
  
• Predictive Frameworks: Strong command of Scikit-learn, XGBoost, LightGBM, and deep learning libraries (PyTorch/TensorFlow).
  
• Data & Architecture: Deep understanding of relational databases and vector databases (e.g., Qdrant, Pinecone, Milvus) optimized for multi-tenant or multi-client security boundaries.
  
• Cloud & DevOps: Experience deploying cloud-native AI services on AWS, Azure, or GCP using Docker and Kubernetes.
  

Qualifications & Experience

• Experience: 5+ years in Software Engineering or Data Science, with at least 2+ years shipping production-grade AI systems.
  
• Hybrid Mindset: Proven experience balancing a product engineering mindset (reusability, clean architecture) with a client-facing delivery mindset (deadlines, clear communication, varying environments).
  
• Education: Bachelor’s degree in Computer Science, Artificial Intelligence, Data Engineering, or a related field.
  
• Leadership & Problem Solving: Proven track record of mentoring junior technical talent and driving solutions for complex, ambiguous architectural problems across both product and client environments.
  

Benefits