Top Machine Learning Jobs in Hartford, CT

Lead ML technical strategy for Live Ads, designing and deploying forecasting, targeting, bidding, auction, and yield-optimization models. Build and evaluate large-scale production ML systems, create online/offline evaluation frameworks, partner with product to set objectives and trade-offs, and communicate results to stakeholders and leadership.

Lead design and execution of a modernized, Python-native AI/ML platform for AIMS: define architecture, drive migration, build migration tooling, ensure scalability, observability, cost optimization, and reliability, prototype GenAI operational tooling, and set org-wide technical standards.

Design and build scalable training and inference systems for LLMs and multimodal/media models. Optimize end-to-end training (data pipelines, distributed training, mixed precision) and inference (KV cache, batching, quantization). Scale models into production, lead efficiency initiatives, and mentor engineers and scientists to improve latency, scalability, and reliability.

Design and execute ML research and end-to-end model development for generative and summary-generation systems. Lead rigorous experiments, translate findings for stakeholders, partner with engineers to deploy models, track state-of-the-art advances, and define roadmaps to improve promotional writing and discovery for members.

Lead technical execution across backend, frontend, mobile, AI, and data. Translate ambiguous product goals into technical plans, unblock hard engineering problems, coordinate cross-domain work, raise engineering quality, define hiring needs and technical evaluation standards, and build internal AI workflows to foster an AI-native engineering culture.

Lead technical pre-sales and enterprise rollouts by owning infrastructure strategy, PoCs, migrations (on‑prem to cloud), DMS/CRM integrations, InfoSec discussions, and stakeholder engagement with C-level sponsors to enable scalable deployments and upsell opportunities.

SoftSnow is seeking an AI Ethicist to define ethical guidelines, assess risks, and ensure compliance in AI implementations across various industries.

Design and implement NLP and LLM-powered pipelines to extract, summarize, and semantically search technical documentation. Fine-tune transformer models, build RAG systems, convert unstructured documents into structured knowledge, optimize inference for production, and collaborate with engineers and SMEs to deploy document intelligence solutions.

Lead program delivery for engineering teams at Arine, optimizing internal processes and ensuring timely execution of technical programs while facilitating Agile methodologies.

The AI Director will oversee ML bot development and implementation, identify AI opportunities, educate teams, and prototype AI solutions.