AI Systems Engineer | Agentic Platforms

SDL Search Partners•Boston, MA

20h

About The Position

The AI Systems Engineer will join a growing Data Science & AI team focused on delivering innovative, enterprise-scale AI solutions. The role partners with business stakeholders to develop technologies that improve decision-making, increase operational efficiency, and generate measurable business impact. It combines technical expertise in artificial intelligence, machine learning, data science, and software engineering with the ability to translate business needs into practical solutions. The engineer contributes throughout the AI development lifecycle, from requirements gathering and data preparation to prompt engineering, agent-based system design, deployment, and production optimization. The role also helps shape the organization's AI capabilities, promotes best practices, and contributes to new AI-powered products and solutions. Successful candidates collaborate directly with business users while building scalable, production-ready applications, balancing speed with quality and applying practical solutions to complex challenges.

Requirements

Advanced Python development skills for AI/ML applications and backend web-based systems.
Experience designing and deploying AI agents capable of reasoning, planning, and executing complex workflows with minimal human intervention.
Hands-on experience integrating vector databases and advanced search technologies, such as Pinecone, for retrieval-augmented generation applications.
Experience with AWS services including EC2, EKS, S3, and Lambda.
Familiarity with Docker and Kubernetes.
Experience implementing infrastructure-as-code solutions using tools such as Terraform.
Strong understanding of large-scale data processing, distributed computing, data ingestion, preprocessing, and feature engineering workflows.
Several years of hands-on experience developing and deploying machine learning, natural language processing, or AI solutions in production environments.
Demonstrated success applying machine learning to complex business challenges and delivering measurable outcomes.
Ability to work independently, take ownership of projects, and drive initiatives from concept through implementation.
Ability to adapt to fast-changing environments, rapidly iterate solutions, and learn new technologies as needed.
Excellent written and verbal communication skills, including the ability to engage technical and non-technical stakeholders.
Proven experience mentoring team members, leading complex technical initiatives, and fostering cross-functional collaboration.
Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related technical discipline.

Nice To Haves

Familiarity with machine learning CI/CD pipelines, experiment tracking platforms such as MLflow and Weights & Biases, and model deployment practices.
Experience developing, deploying, and optimizing machine learning models using frameworks such as XGBoost and Scikit-Learn.
Working knowledge of React or similar frameworks for developing user-facing AI applications.

Responsibilities

Design, develop, fine-tune, and deploy AI models and agentic applications for a range of business needs.
Build scalable AI solutions that can be deployed and maintained in production environments.
Collaborate with business teams to understand objectives, gather requirements, and translate needs into technical solutions.
Communicate methodologies, recommendations, and results to technical and non-technical audiences.
Establish and implement best practices for evaluating AI applications, including accuracy, latency, scalability, reliability, and cost efficiency.
Monitor and optimize deployed solutions to maximize performance and business value.
Develop and maintain robust data pipelines for model training and inference.
Support data quality and availability through preprocessing, labeling, and validation.
Stay current with advancements in AI, machine learning, and large language models; evaluate emerging tools, frameworks, and techniques.
Support and mentor junior engineers and data scientists.
Contribute to internal learning initiatives and promote AI engineering best practices across teams.
Take ownership of projects and drive initiatives from concept through implementation.
Lead complex technical initiatives and foster cross-functional collaboration.