We are seeking a Machine Learning Engineer for our Application AI Product Engineering team. Unlike a traditional research role, this position is centered on the engineering of AI-driven products. You will be responsible for taking sophisticated ML models and embedding them into functional, user-facing applications.

You will work at the intersection of Software Engineering and Data Science, ensuring that AI features—ranging from recommendation engines to generative assistants—are responsive, scalable, and seamlessly integrated into the application front-end and back-end architecture.

Key Responsibilities

• Feature Engineering & Integration: Design and implement APIs and microservices that expose ML model functionality to web and mobile applications.

• Inference Optimization: Optimize model inference for production environments to ensure low-latency user experiences (e.g., using ONNX, TensorRT, or model quantization).

• Product-Centric ML: Collaborate with Product Managers to translate user requirements into technical ML specifications, such as Smart Search or Automated Tagging.

• Streaming & Real-time Data: Build pipelines for real-time feature extraction using tools like Kafka or Flink to power live AI features.

• A/B Testing & Evaluation: Implement framework-level logging to track model performance in the wild and conduct A/B tests to validate product improvements.

• Application Logic: Write high-quality, production-ready code in Python, Go, or Java that handles edge cases where AI might fail (Graceful Degradation).

Requirements

• Experience: 3+ years of experience as an ML Engineer or Backend Engineer with a heavy focus on AI integration.

• Software Pedigree: Strong foundation in Data Structures, Algorithms, and System Design. You should be as comfortable with a Git workflow as you are with a Jupyter Notebook.

• ML Knowledge: Solid understanding of supervised/unsupervised learning and deep learning. Experience with LLM application patterns (RAG, prompt chaining) is highly valued.

• Language: Expert proficiency in Python and familiarity with application frameworks (FastAPI, Flask, or Django).

Technical Stack

• Machine Learning: PyTorch, TensorFlow, or Scikit-learn.

• App AI Tools: LangChain, Haystack, or Microsoft Semantic Kernel.

• Deployment: Docker, Kubernetes, and Serverless functions (AWS Lambda/Google Cloud Functions).

• Database: Experience with both SQL (PostgreSQL) and Vector Databases (Pinecone, Qdrant, or Weaviate).

• Monitoring: Experience with Application Performance Monitoring (APM) and ML observability tools.

Work Details

• Location: [City/Hybrid/Remote] (Ideally suited for tech hubs like Dubai, Bengaluru, or San Francisco).

• Team Structure: You will sit within the Product Engineering unit, working alongside Full-Stack Developers and UX Designers.

• Compensation: Competitive salary + Performance-linked bonuses + Tech equipment allowance.