AI/ML Engineer

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.