Machine Learning Jobs in Stamford, CT

Leads client-facing generative AI and data modernization engagements, directing solution design, delivery planning, architecture, and technical decisions. Guides teams in prompt engineering, context engineering, agent orchestration, LLM evaluation, RAG, vector databases, and AI guardrails. Oversees cloud AI implementations using Gemini, Vertex AI, AWS Bedrock, or Azure AI Services; develops Python prototypes; evaluates model performance; manages workstreams and deliverables; and mentors staff on AI methods, agile delivery, and technical problem-solving.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Design and deploy AI systems and machine learning models, perform data wrangling and analysis, and integrate solutions into data infrastructure. Develop scalable AI applications, relational data systems, evaluation tools, automated tests, and monitoring capabilities. Assess AI behavior, robustness, bias, explainability, drift, hallucinations, and guardrails while evaluating technical controls and communicating findings to clients and stakeholders.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Develop and implement AI and machine learning solutions, including algorithms, data models, infrastructure, evaluation tools, automated tests, monitoring, and responsible AI controls. Lead testing for robustness, bias, drift, explainability, hallucinations, and failure modes. Manage client accounts and technical workstreams, review evidence and controls, communicate trade-offs, mentor teams, and maintain delivery and professional standards.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Develop and implement AI solutions and machine learning systems that enhance products and solve complex client problems. Responsibilities include advanced analytics, LLM production deployment, prompt engineering, RAG pipeline optimization, cross-functional collaboration, technical discovery, responsible AI advocacy, client relationship building, and mentoring junior team members.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Provide strategic legal counsel on data privacy, cybersecurity, and telecommunications law. Advise on incident response, cross-border transfers, privacy-by-design, compliance frameworks, contracts, AI/analytics initiatives, regulatory monitoring, and engagement with law enforcement and stakeholders.

Paid, full-time 11-week summer internship for a rising senior pursuing a bachelor’s degree. The intern will analyze customer reliability issues, research escalations, manage resolution through compliance checks and internal coordination, and identify trends using data and AI/ML research methods. The program also includes mentorship, networking, professional development, community service, and cross-functional projects.

Build AI-powered solutions for sales and marketing productivity, including LLM agents, NLP and Generative AI applications, recommendation systems, prompt and retrieval optimization, and agentic workflows. Prototype and evaluate AI capabilities, transition successful pilots into production, and collaborate with product managers, engineers, data scientists, and business stakeholders to deliver scalable solutions and measurable business outcomes.

Supports payer modernization and health services consulting engagements by analyzing operational challenges, optimizing processes, applying business analytics, engaging stakeholders, implementing quality standards, and guiding transformation initiatives. Develops operational support frameworks, conducts market research, manages change, and advises clients on improving service delivery and business performance while mentoring junior team members.

Leads the AI organization’s enterprise program management function, translating AI strategy into portfolio roadmaps, operating models, governance forums, adoption programs, tool rollouts, executive reporting, and measurable outcomes. Coordinates cross-functional initiatives across technology, business, risk, legal, compliance, finance, HR, and communications. Maintains portfolio visibility, tracks financials and KPIs, manages dependencies and risks, drives enablement and change management, and prepares executive briefings in a regulated financial services environment.

As a Quantitative Research Analyst, you will develop models, conduct complex data analysis, and work with stakeholders to optimize investment strategies.

Develop and implement state-of-the-art machine learning models and statistical methods using massive structured and alternative datasets to discover predictive signals, design experiments, validate ideas through simulation, and collaborate to move research into production trading strategies.

Ten-week technology internship at a multi-strategy hedge fund. Interns join a technology team and contribute to data ingestion, high-performance processing, modeling, portfolio and risk management, live execution, and post-trade analysis. The program includes strategic projects, trading simulations, mentorship, professional development, and presentations to senior leaders. Candidates should have a STEM background, programming experience, machine learning and AI knowledge, strong analytical and communication skills, and interest in financial services.

A 10-week platform internship supporting teams such as Data, Finance, Operations, Risk, Technology, Treasury, HR, Marketing, and Business Development. Interns contribute to strategic projects, automate processes and workflows, build dashboards and tools, improve operational efficiency, use AI tools, participate in a trading simulation, and present work to senior leaders.

Ten-week investment internship supporting a portfolio management team at a multi-strategy hedge fund. Responsibilities include identifying and evaluating data-driven investment insights, contributing to strategic projects, participating in a trading simulation, and presenting work to senior leaders. Interns receive mentorship, exposure to investment and platform teams, professional development, and networking opportunities.

Leads specialty exposure management for non-natural catastrophe and liability portfolios across insurance lines. Oversees analysts performing pre- and post-bind modeling, portfolio production, reporting, regulatory submissions, data quality, and model validation. Partners with underwriting teams to optimize risk-adjusted capacity deployment, presents portfolio insights to senior stakeholders, manages outsourcing relationships, and advances exposure management tools, workflows, AI, automation, and analytics capabilities. Requires extensive insurance or reinsurance catastrophe modeling experience and strong leadership, risk governance, SQL, and analytical skills.

Entry-level Associate Data Analyst supporting analytics, data science, data engineering, and project management rotations. Deliver insights, build dashboards, automate reporting, partner with stakeholders, and apply ML/LLM tools to drive product and client analytics.

Design, develop, validate, operationalize, and maintain large-scale data models and analytics. Perform end-to-end analytics including data wrangling, feature engineering, modeling (including NLP and ML), MLOps, and stakeholder communication to drive business insights and improve operational efficiency.

The Quantitative Researcher will develop and optimize trading strategies, analyze data for alpha signals, and collaborate on financial models.

Responsible for managing the full recruitment lifecycle to attract top talent in quantitative finance and technology roles, collaborating with hiring managers, and ensuring a positive candidate experience.