Senior ML Engineer (Applied AI)

NineTwoThree AI Studio is a product design, engineering, and marketing firm specializing in custom AI, web, and mobile applications for established brands and funded startups. The company is based in Massachusetts with an American and European staff and a strong collaborative remote culture. They have a portfolio of over 150 launched products over 13 years and have been recognized as a top AI agency in the U.S. working with brands like Consumer Reports, FanDuel, and startups in legal tech, logistics, education, and more.

• 3+ years of experience engineering software with focus on machine learning and natural language processing.
• In-depth knowledge of modern LLM architectures, context window mechanics, semantic search, and generative system limitations.
• Experience building and operating production AI systems, including monitoring, evaluation, debugging, and iterative improvement.
• Understanding of evaluation methodologies for LLM systems including retrieval quality, hallucination detection, and performance measurement.
• Exceptional proficiency in Python coding and ability to query, clean, and structure data with SQL.
• Hands-on experience deploying ML or API services in cloud environments, preferably AWS.
• Ability to take ownership from discovery through production deployment and ongoing support.
• Ability to understand new industry verticals’ data constraints and deliver working proof-of-concepts rapidly.
• Product engineering mindset focused on business value and delivery.
• Fluent written and spoken English for client interaction and technical communication.
• Eagerness to experiment with emerging AI tools, models, and frameworks.
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science or related field, or equivalent practical experience.

• Architect & build AI features, designing and implementing classical ML and generative AI solutions balancing autonomous agentic architectures and deterministic pipelines.
• Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment.
• Integrate and deploy AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints.
• Optimize production by refining prompts, system instructions, and chunking strategies balancing accuracy, latency, token consumption, and data privacy.
• Clean and process unstructured or historical client data to train or fine-tune custom algorithms for business problems like forecasting, classification, or anomaly detection.
• Participate in client discovery sessions, translate ambiguous business requirements into technical scopes, and demo prototypes to stakeholders.
• Engage in code reviews, implement validation patterns to test AI outputs, and contribute to an internal AI knowledge base.

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