AI/ML Focused Software Engineer - Business Operations

AI Engineer, Biz Ops — Decision Intelligence Platform

Location: On-site/Hybrid (Paramus, NJ a minimum of 3 days per week) | US-based, no sponsorship available

Ever wanted to be the engineer who actually ships the AI, not just prototypes it? This one's for you.

We're working with a fast-moving decision-intelligence platform looking for an AI Engineer to sit at the intersection of research and production. You'll take models built by their AI Scientists and turn them into real, scalable services: inference pipelines, APIs, batch and streaming systems that businesses actually run on.

This isn't a research role. It's for engineers who like hardening things, who get a kick out of watching a prototype become a product, and who don't mind rolling up their sleeves with Data Engineering, BizOps, and Commercial teams to figure out what "AI-native" actually means for a manual workflow.

What you'll be doing

• Productionizing AI/ML models into APIs, batch and streaming inference systems
• Working directly with AI Scientists to turn research prototypes into real components (feature computation, preprocessing, evaluation loops)
• Building and maintaining pipelines in Python with orchestration tools like Airflow
• Owning CI/CD, containerization, and automated testing for model deployment
• Setting up monitoring and observability (drift, performance regression, alerting)
• Partnering with BizOps and Commercial stakeholders to convert manual workflows into AI-driven services
• Tuning serving latency, throughput, and cost through caching, scaling, and parallelization

What they're looking for

• 3+ years of software engineering experience, including deploying AI systems in production
• Strong Python skills, comfortable across services, pipelines, and ML tooling
• Experience deploying models on AWS or Azure
• Familiarity with Spark, Airflow, or similar orchestration frameworks
• Solid grasp of CI/CD, Docker, Kubernetes, automated testing, version control
• Bonus points for ONNX / model quantization experience, SQL and Snowflake familiarity, or a background in regulated industries like biopharma, healthcare, or finance

MLOps exposure (model registries, feature stores, experiment tracking) is a strong plus but not a hard requirement.

Note: candidates must be authorized to work in the US without current or future sponsorship.