Role Overview
We are seeking a highly skilled Senior AI/ML Engineer to lead the design, development, and deployment of intelligent, scalable systems. In this role, you will bridge the gap between AI research and production-grade software engineering. You will own the entire lifecycle of our intelligent features-from architectural design and building robust evaluation frameworks to production deployments.
As a senior member of the team, you will design architectures where no playbook exists, translate ambiguous product goals into concrete technical strategies, and mentor junior engineers.
Responsibilities
1. Model Development & System Architecture
- Design and scale AI/ML pipelines, including data collection, preprocessing, feature engineering, and model validation.
- Build production-grade GenAI systems utilizing LLMs, Retrieval-Augmented Generation (RAG), vector embeddings, agentic orchestration, and long-context management.
- Architect and optimize search, classification, regression, or recommendation engines based on business priorities.
2. MLOps & Production Engineering
- Own the evaluation and observability matrix-building robust test harnesses to measure latency, accuracy, cost, bias, and drift before and after code hits production.
- Deploy and maintain AI/ML infrastructure using containerization (Docker, Kubernetes) and automated CI/CD MLOps workflows.
- Expose models securely via high-performance, reliable REST/gRPC APIs.
3. Leadership & Collaboration
- Partner with Product, Data Engineering, and Security teams to deliver compliant and impactful user experiences.
- Mentor and coach junior engineers, lead code reviews, and champion engineering best practices across the organization.
Technical Skills & Qualifications
Education & Experience
Experience: 7+ years of professional experience in software engineering, with at least 5+ years specifically focused on implementing AI/ML models in production environments.
Core Stack
Languages: Expert-level proficiency in Python (knowledge of Java, C++, or Go is a major plus).
Frameworks: Hands-on experience with core ML/Deep Learning libraries like PyTorch, TensorFlow, Scikit-learn, or Hugging Face.
Generative AI: Practical experience working with API orchestration, prompt engineering, vector databases (e.g., Pinecone, Milvus, Qdrant), and frameworks like LangChain or LlamaIndex.
Cloud Platforms: Extensive experience deploying workloads on cloud infrastructures such as AWS (SageMaker), Google Cloud Platform (Vertex AI), or Azure.
Data & Databases: Strong SQL/NoSQL proficiency and familiarity with handling massive data streams.
