Top Senior Machine Learning Jobs in New Orleans, LA

Leads and manages an applied machine learning team developing fraud detection and identity verification models. Responsibilities include people management, coaching, code review, production ML systems, model lifecycle ownership, experimentation, deployment, monitoring, fraud research, cross-functional prioritization, and communicating results. The role remains hands-on while partnering with Product, Engineering, Risk, and leadership to drive measurable business impact and shape future product strategy using machine learning and AI.

Lead the strategy, design, deployment, and scale-up of AI-powered health products across the Global South. Own product roadmaps, execution, government-system integration, stakeholder adoption, partnerships, grant development, and portfolio expansion. Collaborate with engineering, machine learning, program, and domain teams to deliver sustainable, measurable public-health impact.

Performs advanced physical and remote insurance premium audits for business clients. Reviews business records, interviews owners and managers, investigates exposures, documents findings, prepares written audit reports, schedules assignments, communicates results, and submits completed work for quality review. The auditor must manage deadlines independently, correct returned audits, maintain accurate records, and use Microsoft Office applications. This remote role requires reliable transportation, a valid driver’s license, strong analytical and communication skills, and at least five years of premium insurance auditing experience.

Leads the company’s adoption of AI, analytics, automation, and business intelligence. Builds AI-powered workflows, Excel solutions, natural-language data interfaces, dashboards, forecasts, predictive analytics, and automated reporting using Snowflake, SQL, Tableau, and related tools. Partners across business functions to identify opportunities, improve data quality, automate repetitive processes, and deliver scalable decision-support solutions. Establishes responsible AI practices and advances an AI-first analytics culture.

Drive new-account acquisition and cloud migrations for AI and infrastructure solutions. Generate pipeline through global outbound prospecting, execute multichannel outreach, manage the full enterprise sales cycle, and close large cloud, infrastructure, and data center deals. The role requires deep knowledge of AI computing, cloud infrastructure, power capacity, cooling systems, financial modeling, and B2B enterprise net-new cloud sales. Candidates work from Akamai’s San Francisco office and collaborate across time zones.

Design and deploy machine learning models for financial document processing, optimize large language models, collaborate with cross-functional teams, and guide junior peers in a fast-paced environment.

The Senior Staff Machine Learning Scientist will lead research initiatives in computer vision and multimodal understanding, develop novel models, and translate research into practical applications while mentoring team members.

Own the strategy, requirements, evaluation, and execution of AI agent products and systems. Partner closely with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, reliability, and user experience. Define evaluation frameworks, drive prioritization, and ensure AI features are understandable, predictable, safe, and trusted. Translate model capabilities and feedback into product decisions and reliable real-world workflows.

Own the strategy, requirements, evaluation, and reliability of AI agent products. Partner closely with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, reliability, and user experience. Define offline and online evaluation frameworks, drive product execution, and ensure AI features are correct, predictable, safe, and trusted. Translate model capabilities and feedback into product decisions, roadmaps, and rapid iteration.

Lead the Data Sources product roadmap for document parsing, fraud detection, and bank aggregation. Drive metrics (parse accuracy, fraud catch rate, connection success, SLAs), write specs, engage clients, mentor PMs/engineers, and represent the platform externally. Prioritize document types, extraction models, reliability, and scalability to improve conversion, client satisfaction, and operational efficiency across mortgage, tenant screening, and government use cases.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

The Director of AI will lead AI and machine learning initiatives, develop AI architecture for humanoid robots, oversee production-ready AI systems, and manage team growth and budget.

Design and deliver enterprise-grade generative AI solutions on AWS using Bedrock, AgentCore, SageMaker, LLMs, RAG pipelines, vector databases, and agentic workflows. Build scalable APIs and integrations, optimize prompts and models, evaluate performance, and implement security and governance. Collaborate with application, data, and platform teams, troubleshoot production systems, define best practices, and mentor engineers while remaining hands-on.

Develop and expand software products and platforms across backend, frontend, cloud, AI, and machine learning systems. Build efficient, reliable, and testable code; collaborate with business partners to launch features; influence product and engineering decisions; mentor team members; and help shape team culture and product vision in an agile environment.

Build and productionize customer-facing data and AI solutions on the Databricks platform. Own architecture and design decisions, develop end-to-end data pipelines and applications, integrate machine learning models, and guide enterprise customers through implementation and adoption. Collaborate with engineering, product, support, project managers, and customer stakeholders while delivering projects, documenting solutions, resolving issues, and creating reusable frameworks. Travel to customers approximately 20% of the time.

Develop and deploy deep learning models for AI-guided antibody drug discovery and target identification. Design experiments, build systems biology models, and create in silico and in vitro validation approaches. Collaborate with biological and wet-lab teams, present findings, publish high-impact research, and mentor scientists and engineers.

Leads the engineering strategy, architecture, development, operations, and continuous improvement of an enterprise agentic AI foundation. Establishes scalable engineering standards, operating models, governance, and reusable platform services that enable AI products and intelligent workflows. Partners with product, data, infrastructure, security, and business teams to modernize existing systems, guide cross-functional delivery, monitor operations, and communicate priorities, risks, and investment needs to senior leaders. Manages and develops product or engineering leaders while driving measurable business value.

Designs, evaluates, and improves ranking and recommendation systems through experimentation, feature engineering, and production machine learning. Builds reliable data and feature pipelines, incorporates multimodal signals, manages uncertainty, calibration, and model drift, and monitors system performance. Partners cross-functionally with Engineering, Product, Business, and Analytics to deliver scalable, latency-sensitive solutions with measurable business impact.

The Senior Director leads research in Application Platforms & AI, guiding clients in AI strategy and market trends, while producing critical research content and engaging with clients directly.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.