Top Machine Learning Jobs in Hartford, CT

Owns the commercial health and client relationships for Databricks data and AI engagements. Responsibilities include managing budgets, margins, forecasts, scope changes, executive stakeholders, steering committees, delivery transitions, business outcomes, close-outs, and follow-on opportunities. Partners with Sales, project managers, solutions architects, and technical leaders to deliver measurable client value across data engineering, lakehouse, governance, BI, and AI initiatives.

Manage the full enterprise sales cycle, from prospecting and pipeline development through deal closure and account expansion. Build relationships with executives and decision-makers, run multichannel campaigns, develop pursuit strategies, coordinate with partners and internal teams, maintain accurate forecasts, and achieve revenue targets for data engineering, platform modernization, and AI/ML consulting services.

Drive the full enterprise sales cycle for data engineering, platform modernization, and AI/ML consulting services. Build pipeline through prospecting, outreach, networking, campaigns, and events; develop pursuit strategies; close complex deals; and expand strategic partnerships. Own executive client relationships, coordinate sales, delivery, customer success, solutions engineering, and marketing, while maintaining accurate forecasts and achieving revenue targets.

Represents Snowflake to senior marketing and advertising leaders in regulated industries. Develops privacy-safe measurement, identity resolution, audience activation, personalization, AI, and data clean room solutions on Snowflake. Leads strategic customer engagements, creates reference architectures and reusable playbooks, partners with sales, product, engineering, and technology partners, and serves as a marketing and AI thought leader through workshops, enablement, webinars, and industry events.

Leads strategic engagements with CMOs and marketing executives across technology, media, and manufacturing. Translates marketing measurement, audience strategy, personalization, identity, and advertising challenges into Snowflake data and AI solutions. Builds demonstrable use cases, repeatable sales and enablement assets, and thought leadership for customers, field teams, and partners. Collaborates with sales, solution engineering, product, and ecosystem partners while advising executives and supporting strategic growth.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Own the strategy, roadmap, backlog, and delivery of GridUnity’s AI platform capabilities. Partner with customers, engineering, design, data science, sales, and leadership to validate AI use cases, define requirements, manage experiments and releases, and measure model and business outcomes. Ensure responsible AI practices including explainability, auditability, privacy, monitoring, human review, and fallback behavior. Provide technical product expertise, support customer-facing activities, and mentor other product owners.

Supports the full AI development lifecycle by helping execute pilots and proofs of concept, documenting standards and playbooks, evaluating AI use-case pipeline health, researching tools and platforms, developing acceleration dashboards, supporting value assessments, and preparing leadership updates. The intern will also present a final project and gain experience with responsible AI, governance, and enterprise AI delivery.

Drive new enterprise business for Automation Anywhere’s Agentic Process Automation platform. Responsibilities include prospecting, managing sales pipelines, conducting customer presentations and demonstrations, attending industry events, leading proof-of-concept exercises and RFX responses, and coordinating solution design with technical teams. The role requires consultative enterprise software selling, quota attainment, technical communication, and collaboration with sales engineers, partners, and professional services.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Manage strategic banking accounts for NVIDIA’s Financial Services sales team. Own customer relationships, develop account strategies, forecast sales, drive revenue and platform adoption, expand market share, and build partnerships with cloud providers, OEMs, and ISVs. Evangelize NVIDIA’s accelerated computing technologies to executives, data scientists, and IT leaders while leading go-to-market plans, customer education, and cross-functional collaboration.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Leads the strategy, architecture, development, delivery, modernization, and support of enterprise quality engineering platforms, AI-enabled tools, developer enablement solutions, and shared services. Oversees multiple software engineering teams, cloud-native application development, platform reliability, CI/CD, AI integration, security, and operational excellence. Partners with product, architecture, security, DevOps, and business leaders while managing delivery, technical direction, resource allocation, talent development, and organizational growth.

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.

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.

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.

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.

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

Develop and benchmark algorithms and reproducible pipelines for RNA splicing analysis across bulk, single-cell, and spatial transcriptomic data. Integrate genomic, epigenomic, proteomic, and long-read data to investigate disease mechanisms, interpret results, support experimental validation, publish findings, maintain open-source tools, collaborate with researchers, and mentor junior staff.

Lead growth data science initiatives by partnering with Marketing, Sales, and Customer Experience leaders. Develop analyses, experimentation frameworks, and machine learning models for acquisition, engagement, retention, monetization, targeting, segmentation, uplift modeling, and lifetime value prediction. Productionize reliable models with engineering teams, communicate insights to senior stakeholders, establish experimentation best practices, and mentor junior data scientists. The role requires strong business judgment, advanced technical expertise, and experience operating in ambiguous, zero-to-one environments.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.

The General Counsel will lead the in-house legal function, advise executive leadership and the Board, manage legal teams and outside counsel, and oversee corporate, regulatory, privacy, AI, employment, intellectual property, litigation, and contract matters. The role will guide product and sales initiatives, develop scalable legal workflows, manage risk, support HR, and advise on consumer protection, fintech regulations, data privacy, and emerging AI frameworks as the company expands.

Build backend services, data pipelines, detection features, and AI agents for email security. Integrate machine learning and large language models into investigation workflows, evaluate attack detection accuracy, improve reliability and performance, and own features from design through production release. Collaborate with ML engineers, data scientists, and software engineers while contributing tested code, technical designs, and code reviews.

Lead managed services for clients' Azure, Databricks, and Microsoft Fabric data environments. Architect, implement, monitor, troubleshoot, and govern data platforms, ETL pipelines, data warehouses, semantic models, reporting, and AI/ML solutions. Manage incidents and SLAs, advise clients on licensing and technical strategy, communicate solutions, document environments, and mentor distributed engineering teams.

Leads technical discovery, enterprise solution architecture, technology assessments, and implementation roadmaps for consulting clients. Designs architectures across applications, data, cloud, integration, and enterprise technology; advises executives on technology decisions, scalability, security, cost, and risk. Guides technical teams, resolves complex delivery challenges, develops proofs of concept and reusable standards, evaluates emerging technologies, and supports proposals, estimates, statements of work, and new business development.