Top Machine Learning Jobs in Hartford, CT

Drive AI-enabled product strategy and execution by identifying customer opportunities, defining requirements, and partnering with engineering, design, data, sales, marketing, and customers. Lead development of products using LLMs, generative AI, RAG, and intelligent agents; validate roadmap priorities, communicate product vision, support launches, and improve feature adoption. Apply AI tools to accelerate product specifications, workflows, and software delivery.

Leads revenue, go-to-market execution, forecasting, and organizational growth for strategic enterprise accounts. Builds and manages sales leadership and enterprise account teams, drives AI-focused value selling, oversees complex negotiations, and develops Fortune 500 and Global 2000 relationships. The role partners cross-functionally on customer expansion, product strategy, marketing, customer success, hiring, budgeting, and operational planning. Requires extensive enterprise SaaS sales leadership, experience with developer tools or cloud infrastructure, AI solution expertise, and executive-level engagement.

Implements and maintains cloud security controls across Microsoft 365 GCC High, Azure, AWS, and hybrid environments. Administers cloud security platforms, integrates telemetry, supports vulnerability remediation, and validates controls against CMMC, NIST, DFARS, ITAR, ISO 27001, and other requirements. Secures generative AI, machine-learning, copilot, RAG, and agentic systems through access controls, sandboxing, logging, and execution safeguards. Performs risk assessments, control testing, audit evidence collection, SOC escalation support, automation, documentation, and occasional off-hours incident support.

Designs, implements, and maintains cloud security controls across Microsoft 365, GCC High, Azure, AWS, and hybrid environments. Leads cloud and AI security architecture, threat modeling, infrastructure-as-code automation, security testing, posture management, and incident support. Develops secure patterns for generative AI, machine learning, copilots, agents, and CUI workloads. Translates CMMC, NIST, DFARS, ITAR, ISO 27001, GDPR, and related requirements into technical controls, evidence, and risk mitigations while mentoring engineers and guiding cross-functional teams.

Leads enterprise healthcare interoperability strategy, operations, connectivity, and strategic partnerships. Establishes operating models, roadmaps, KPIs, governance, and partner ecosystems across EHRs, HIEs, QHINs, payers, providers, and data networks. Translates regulatory and technical requirements into scalable capabilities, supports product and commercial growth, oversees investments and agreements, and builds high-performing teams. Requires executive leadership in healthcare data exchange, interoperability standards, regulatory strategy, operations, partnerships, and technology commercialization.

Designs end-to-end architectures and high-level designs for web security products and features, spanning configuration, reporting, runtime systems, analytics, and attack detection. Collaborates with product management and cross-functional teams to define requirements, guides engineering implementation, presents designs, and contributes security product expertise and technical roadmap direction.

Develop and demonstrate AI and data solutions for aerospace engineering projects. Responsibilities include designing architectures, building proof-of-concepts and machine learning systems, writing code, creating demos, supporting cloud and data engineering initiatives, and delivering production-ready solutions. The intern will collaborate cross-functionally, participate in design thinking, gain mentorship, and work on real-world projects using technologies such as Python, cloud platforms, machine learning, LLMs, and computer vision.

Leads customer engagements and large-scale analytics projects from inception through delivery, ensuring measurable business value, quality, budgets, and timelines. Builds customer relationships, translates needs into analytics solutions, supports renewals and expansions, and coordinates with product, engineering, and data science teams. Also supports solution architecture, pre-sales demonstrations, proof-of-concept implementations, technical use-case development, and customer conferences. Presents complex insights to technical and nontechnical stakeholders.

Lead CluePoints’ alliance and channel strategy across technology partners, systems integrators, consultancies, and regional resellers. Build partner-sourced revenue, develop channel programs, enable partners, manage complex commercial relationships, and collaborate with Sales, Marketing, Product, and Services. The role will establish partnerships in markets including Japan, India, and Europe, while eventually building and leading a partnerships team.

Lead enterprise and solution architecture for CVS Health’s customer communications ecosystem. Define target-state architecture, platform roadmaps, integrations, APIs, data models, cloud infrastructure, consent controls, messaging capabilities, and governance across Adobe, Salesforce, and GCP. Guide engineering teams, vendor evaluations, proofs of concept, and build-versus-buy decisions while ensuring scalability, security, compliance, and performance. Evaluate AI and machine learning applications for personalization and customer engagement, and communicate architectural recommendations to senior stakeholders.

Leads the creation and growth of Finance Insights & Decision Support for Pharmacy Services Finance. Establishes methodologies, investigates ambiguous business questions, applies analytics and AI-enabled techniques, translates findings into executive recommendations, and identifies opportunities to operationalize insights. Defines the function’s long-term vision, talent strategy, capability roadmap, and organizational structure while mentoring others and partnering with senior stakeholders.

Leads portfolio planning and cross-functional execution for Data Science Core and GenAI Studio programs. Establishes roadmaps, operating rhythms, priorities, dependencies, risk management, readiness criteria, decision processes, and success measures. Coordinates data science, engineering, product, research, privacy, legal, security, and business stakeholders from problem framing through deployment, adoption, and monitoring. Communicates technical tradeoffs and responsible AI considerations while improving team effectiveness, capacity planning, documentation, and delivery practices.

Leads responsible AI adoption across U.S. operations, identifying opportunities, developing roadmaps, and delivering AI solutions from concept through adoption. Partners with business, technology, data teams, and external vendors across underwriting, claims, policy, actuarial, finance, and operations. Builds business cases, manages governance, drives process redesign and change management, and promotes AI literacy and user adoption while ensuring alignment with business value, risk, compliance, and organizational objectives.

Create, solve, review, and validate coding-heavy machine learning tasks for AI training. Build and optimize models, training and inference workflows, data pipelines, evaluation systems, and reproducible Python workflows. Diagnose numerical and system-level failures, improve memory use and throughput, and verify technical correctness while working with coding agents.

Lead end-to-end data science and machine learning solutions, translating business problems into production-ready models, GenAI applications, and reusable technical patterns. Establish evaluation, governance, monitoring, documentation, and quality standards while mentoring data scientists and ML engineers. Collaborate with senior stakeholders, data engineering, platform teams, and distributed onshore/offshore groups to deliver maintainable solutions across statistical modeling, forecasting, NLP, optimization, and responsible AI.

Lead end-to-end technical strategy for customer accounts, design scalable data and AI architectures, and drive Databricks adoption and consumption. Advise senior technical stakeholders, lead architecture discussions, build custom solutions for competitive opportunities, coordinate cross-functional resources, and provide product feedback. The role requires strong Python and SQL skills, distributed data systems expertise, public-cloud deployment experience, and the ability to translate technical concepts into business value.

Leads quantitative and qualitative market research on international development, AI adoption, institutional demand, funding, procurement, competition, and commercial opportunities. Evaluates AI and data service applications, develops market assessments and strategic recommendations, manages research initiatives, prepares executive presentations, and collaborates with leadership, technical, operational, and business development teams to support market entry and growth.

Own Fetch’s on-platform advertising measurement strategy, including event taxonomies, attribution, incrementality, reporting, and data pipeline standardization. Lead migration to standardized ad event infrastructure, define measurement methodologies such as multi-touch attribution and geo lift, and partner with engineering, data science, advertisers, and third-party measurement providers. Establish scalable measurement standards and ensure new ad products are instrumented and attributable from launch.

Lead AI/ML initiatives for Air Force condition-based maintenance, from predictive model development through production deployment. Architect data pipelines, develop machine learning, deep learning, NLP, and generative AI solutions, and build knowledge graph capabilities. Provide technical leadership, integrate models into applications and workflows, and monitor data quality, model performance, drift, and bias. Collaborate with engineering, cloud, software, and product teams while supporting defense aviation logistics and sustainment missions.

Own research and implementation of post-training methods for legal language models, including supervised fine-tuning, reinforcement learning, reward design, verifiers, preference modeling, evaluations, and distributed training. Lead experiments from research direction through production deployment, while debugging training instability, reward hacking, and divergence. Build rigorous evaluation systems and production-quality Python code for long-horizon legal reasoning and agentic workflows.

Build synthetic data pipelines, evaluation systems, benchmarks, verifiers, rubrics, and quality metrics for legal AI models and agentic workflows. Partner with researchers, legal experts, and customers to investigate model failures, validate metrics, maintain data quality and provenance, and convert expert feedback and product usage into training signals and improved evaluations.

Leads enterprise Key Drivers Analytics and an Associate Director team delivering advanced analytics across pharmaceutical business priorities. Owns analytics roadmaps, methodologies, governance, modeling standards, talent deployment, and globally distributed delivery. Directs multivariate analyses, segmentation, diagnostics, scenario modeling, validation, insight generation, and executive recommendations. Partners cross-functionally to translate evidence into measurable business actions while ensuring reproducibility, compliance, responsible AI, and scalable analytical solutions.

Leads enterprise advanced analytics and modeling to identify drivers of business performance across brands, markets, customers, accounts, and geographies. Translates findings into prioritized actions, playbooks, launch strategies, and scenario-based recommendations. Oversees rigorous, scalable analytical approaches, partners with senior stakeholders, and develops analytics talent across the organization.

Leads the enterprise Advanced Analytics and Modeling portfolio, strategy, roadmap, governance, investment priorities, and global operating model. Oversees analytics capabilities spanning precision health, commercial analytics, optimization, AI, NLP, GenAI, and unstructured intelligence. Partners with senior business and technology leaders to translate strategic decisions into validated, interpretable, reusable analytical solutions and measurable impact. Manages senior capability leaders and distributed US-Hyderabad teams while developing talent, allocating resources, and guiding scalable ModelOps and enterprise deployment.

Lead analyst relations programs to improve Databricks’ positioning in industry evaluations, including Gartner, Forrester, and IDC. Develop differentiated narratives, submissions, demonstrations, customer evidence, and executive briefings. Scale analyst relations through AI-assisted workflows, convert analyst feedback into product and go-to-market recommendations, and influence product, marketing, sales, and executive teams. Manage multiple deadline-driven workstreams while improving analyst advocacy, market visibility, and evaluation outcomes.