AI Engineer (Backend) - Assistant Manager

The team: Enterprise technology has to do much more than keep the wheels turning; it is the engine that drives functional excellence and the enabler of innovation and long-term growth. Learn more about ET&P Your work profile We are seeking a highly skilled AI Backend Engineer to design, build, and deploy high-scale, production-grade AI services. As a key member of our engineering team, you will focus on integrating Large Language Models (LLMs), Agentic AI, optimizing RAG (Retrieval-Augmented Generation) pipelines, and architecting robust backend systems that power our core AI platform. Job Responsibilities: AI Integration & Development: Design and implement APIs for AI/ML model inference, specifically focusing on LLM workflows, NLP, and Agentic AI systems. Backend Architecture: Build and maintain scalable, asynchronous distributed systems and microservices using Python/FastAPI/Flask/Django. Data Pipeline Management: Develop robust ETL/ELT pipelines and manage data processing to train, fine-tune, and serve models. Production Deployment: Deploy AI models into production environments utilizing containerization (Docker, Kubernetes) and cloud services (Azure/AWS/GCP). Performance Optimization: Optimize backend services for high performance, low latency, and maximum scalability. Collaboration & Mentorship: Work closely with full-stack developers, data scientists, ML engineers, and front-end teams to integrate AI capabilities into end-user products, providing technical leadership and mentoring junior developers. Key skills required: Experience: 3-6 years of professional backend development experience, with at least 2+ years specialized in AI/ML applications. Languages: Expert-level proficiency in Python (FastAPI, Flask, Django). AI Frameworks: Hands-on experience with LLM frameworks like LangChain, LlamaIndex, Hugging Face, or PyTorch/TensorFlow . Database Systems: Proficient in Chroma, FAISS, Qdrant (Vector DB), SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis, Elasticsearch) databases. Cloud & DevOps: Proven experience with Azure , AWS (S3, EC2, Lambda) or GCP, and CI/CD pipelines, MLOps. Architecture: Strong knowledge of distributed systems, async processing, RESTful APIs, and system design patterns. Education: Bachelors or Master’s degree in Computer Science, AI, or related fields.