AI Systems Engineer | Agentic Platforms

Position Overview

We are looking for an experienced AI Engineer to join a growing Data Science & AI team focused on delivering innovative, enterprise-scale AI solutions. In this role, you will partner closely with business stakeholders to develop technologies that improve decision-making, increase operational efficiency, and generate measurable business impact.

The ideal candidate combines deep technical expertise in artificial intelligence, machine learning, data science, and software engineering with a strong understanding of how to translate business needs into practical solutions. You will be involved throughout the full lifecycle of AI development, from requirements gathering and data preparation to prompt engineering, agent-based system design, deployment, and production optimization.

This position offers the opportunity to help shape the future direction of AI capabilities across the organization while promoting best practices and contributing to the development of new AI-powered products and solutions.

What Success Looks Like

Successful candidates are comfortable collaborating directly with business users while building scalable, production-ready AI applications. You bring strong analytical and problem-solving skills, thrive in fast-moving environments, and balance speed with quality. You are naturally curious, innovative in your approach, and motivated by solving complex challenges with practical, high-impact solutions.

Key Responsibilities

AI Solution Development

• Design, develop, fine-tune, and deploy AI models and agentic applications that address a wide range of business needs.
• Build scalable AI solutions that can be effectively deployed and maintained in production environments.

Business Partnership & Stakeholder Engagement

• Collaborate with business teams to understand objectives, gather requirements, and translate needs into technical solutions.
• Clearly communicate methodologies, recommendations, and results to both technical and non-technical audiences.

Model Evaluation & Performance Optimization

• Establish and implement best practices for evaluating AI applications, including accuracy, latency, scalability, reliability, and cost efficiency.
• Continuously monitor and optimize deployed solutions to maximize performance and business value.

Data Engineering & Preparation

• Develop and maintain robust data pipelines to support model training and inference.
• Ensure data quality and availability through effective preprocessing, labeling, and validation processes.

Research & Innovation

• Stay current with advancements in artificial intelligence, machine learning, and large language models.
• Evaluate emerging tools, frameworks, and techniques to drive continuous improvement and innovation.

Mentorship & Knowledge Sharing

• Support and mentor junior engineers and data scientists.
• Contribute to internal learning initiatives and promote AI engineering best practices across teams.

Technical Requirements

Required Experience

AI Engineering

• Advanced Python development skills for both AI/ML applications and backend web-based systems.

Agentic AI Systems

• Experience designing and deploying AI agents capable of reasoning, planning, and executing complex workflows with minimal human intervention.

Retrieval-Augmented Generation (RAG)

• Hands-on experience integrating vector databases and advanced search technologies, such as Pinecone, to enhance AI application performance.

Cloud Infrastructure & DevOps

• Experience with AWS services including EC2, EKS, S3, and Lambda.
• Familiarity with containerization technologies such as Docker and Kubernetes.
• Experience implementing infrastructure-as-code solutions using tools such as Terraform.

Data Engineering

• Strong understanding of large-scale data processing, distributed computing, data ingestion, preprocessing, and feature engineering workflows.

Preferred Experience

MLOps

• Familiarity with machine learning CI/CD pipelines, experiment tracking platforms such as MLflow and Weights & Biases, and model deployment practices.

Traditional Machine Learning

• Experience developing, deploying, and optimizing machine learning models using frameworks such as XGBoost and Scikit-Learn.

Front-End Development

• Working knowledge of React or similar frameworks used to develop user-facing AI applications.

Qualifications

Education

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related technical discipline.

Professional Experience

• Several years of hands-on experience developing and deploying machine learning, natural language processing, or AI solutions in production environments.

Analytical & Problem-Solving Skills

• Demonstrated success applying machine learning techniques to solve complex business challenges and deliver measurable outcomes.

Ownership & Initiative

• Ability to work independently, take ownership of projects, and drive initiatives from concept through implementation.

Adaptability

• Comfortable operating in fast-changing environments, rapidly iterating solutions, and learning new technologies as needed.

Communication

• Excellent written and verbal communication skills with the ability to engage effectively with both technical and non-technical stakeholders.

Leadership & Collaboration

• Proven experience mentoring team members, leading complex technical initiatives, and fostering cross-functional collaboration.