Top Machine Learning Engineer Jobs

Research, prototype, build, and deploy predictive products using statistical analysis and machine learning. Analyze large financial datasets, formulate hypotheses, develop and evaluate models, productionize algorithms, write reliability tests, conduct peer reviews, and maintain expertise in machine learning, data analysis, MLOps, and agentic LLM systems.

Lead end-to-end delivery of production AI and machine learning use cases, including LLM and agentic systems. Define scalable architectures, evaluation frameworks, guardrails, monitoring, and operational practices. Create reusable AI engineering patterns, partner with business and technical stakeholders, influence adoption, guide architecture decisions, and mentor engineers. Help establish team governance, prioritization, and delivery practices while ensuring reliable, secure, measurable business impact.

Leads the design, development, deployment, and operation of machine learning models, platforms, data pipelines, and production services at scale. Responsibilities include model training, monitoring, optimization, cloud infrastructure, distributed systems, CI/CD, responsible AI, and collaboration with product and data science teams. The role also focuses on RAG pipelines, agentic AI systems, LLM orchestration, and resilient production deployments.

Build and deploy production-grade AI/ML software, APIs, and applications for healthcare provider use cases. Develop prompt-based AI capabilities, integrate enterprise data and cloud services, create automated tests and evaluation pipelines, and ensure security, observability, scalability, and reliability. Collaborate with product, architecture, data science, and platform teams while owning technical work from proof of concept through production.

Design, build, deploy, scale, and maintain machine learning models and platforms in production. Responsibilities include developing ML infrastructure, data pipelines, cloud architectures, distributed systems, model monitoring, retraining, testing, CI/CD, and responsible AI practices. The role collaborates with Product, Data Science, and Agile engineering teams while applying advanced modeling techniques and managing large-scale containerized ML systems.

Leads the design, development, and deployment of AI and machine learning solutions for healthcare technology platforms. Builds scalable ML models, MLOps pipelines, LLM and RAG systems, backend services, and agent-based applications. Establishes standards for monitoring, testing, CI/CD, and lifecycle management while collaborating with engineering, data, product, security, and SRE teams. Provides architectural guidance and technical mentorship, evaluates emerging AI technologies, and promotes responsible, secure, and resilient AI implementation.

Leads the design, development, and deployment of enterprise AI/ML solutions, including agentic AI, LLMs, RAG, conversational AI, and intelligent workflows. Establishes AI architecture, governance, observability, MLOps, evaluation, monitoring, and responsible AI practices. Builds reusable AI platforms and services, optimizes prompts and models, ensures secure and compliant enterprise integrations, and mentors engineering teams. Collaborates with technical and business stakeholders to deliver scalable, production-ready AI capabilities for healthcare and consumer digital experiences.

Develop and deploy AI and machine learning solutions for healthcare using Generative AI, traditional ML, AWS, Python, FastAPI, Kubernetes, and Databricks. Build scalable model APIs, analytical pipelines, and agentic workflows; apply responsible AI practices; optimize software delivery; and collaborate with technical and business stakeholders. The role includes model development, data analysis, API maintenance, documentation, monitoring, and evaluation of emerging AI technologies.

Leads the design, development, deployment, and operationalization of enterprise AI solutions, including LLM applications, RAG systems, intelligent agents, classification models, and decision-support tools. Establishes prompt strategies, guardrails, evaluation frameworks, monitoring, observability, and governance practices. Partners with Finance, Data Engineering, Architecture, Security, and Responsible AI teams to deliver scalable, secure, compliant, and production-ready solutions.

Designs, deploys, and monitors AI safeguards for prompt injection, jailbreaks, toxicity, sensitive topics, and data exposure. Builds evaluation datasets and metrics, integrates and fine-tunes detection models, balances quality, latency, and cost, develops configurable customer policies, and partners with Security to convert adversarial findings into mitigations. Requires extensive production software and applied machine learning experience, strong LLM safety knowledge, evaluation expertise, and advanced Python skills.

Develop and operationalize AI/ML, computer vision, and automatic target recognition solutions using multi-INT and geospatial data. Build MLOps pipelines for continuous model integration and deployment, create analytics products, and apply best practices for version control, reproducibility, and governance. Collaborate with engineers, clients, and users while designing, testing, and deploying scalable models and software in cloud or hybrid environments.

Designs, deploys, and maintains production AI/ML solutions for document digitization using generative AI and LLMs. Responsibilities include model lifecycle management, scalable Python and cloud pipelines, AWS infrastructure, monitoring, validation, security, and continuous improvement. The role also involves dashboard development, modernization of legacy document workflows, cross-functional collaboration, mentoring junior engineers, and adoption of emerging AI technologies.

Lead the architecture and development of large-scale machine learning systems, distributed training infrastructure, ranking systems, and production ML services. Drive complex initiatives from concept through deployment, establish engineering standards, partner with product and engineering leadership, and mentor data scientists and engineers. Build reliable, low-latency, high-availability AI platforms using advanced ML frameworks, accelerators, and big data technologies.

Build, deploy, scale, and monitor machine learning models and production services. Responsibilities include model development, feature selection, data pipeline construction, distributed data processing, cloud and Kubernetes operations, CI/CD automation, testing, model governance, responsible AI, and collaboration with product and data science teams.

Designs and deploys enterprise-scale AI/ML and Generative AI solutions. Responsibilities include AI/ML architecture, MLOps, CI/CD, model monitoring, drift management, APIs, microservices, cloud and containerized deployments, technical design reviews, reusable engineering standards, and mentoring. Partners with data scientists, engineers, product, security, and operations teams to deliver production-ready systems.

Design, develop, validate, and deploy machine learning, GenAI, LLM, and agentic solutions for enterprise healthcare operations. Build scalable Python inference APIs on Kubernetes, implement semantic search and retrieval capabilities, and maintain AI/ML pipelines. Evaluate emerging technologies, apply responsible AI practices, use AI-assisted development tools, and collaborate with technical and business stakeholders while ensuring strong documentation, coding standards, and performance monitoring.

Build and deploy generative AI, agentic AI, and large language model solutions in Python. Develop semantic models, ontologies, AI pipelines, evaluation frameworks, and MLOps capabilities. Establish responsible AI governance, model monitoring, and semantic integration standards across enterprise systems. Partner with business and technical stakeholders to deliver measurable outcomes, mentor engineers and data scientists, and influence technical direction across corporate technology.

Design, develop, and operate scalable VoIP infrastructure supporting AI-driven voice workloads. Integrate telephony systems, LLM applications, and real-time communication platforms while improving observability, reliability, deployment, and SRE practices. Build tested, maintainable software across telephony and AI layers, support production environments, collaborate with engineering and product teams, and mentor colleagues.

Build Moveworks’ agent evaluation and reward-modeling platform. Design configurable rubrics, deterministic validators, LLM judges, confidence scoring, and calibration against human-labeled trajectories. Analyze simulator-to-production divergence, maintain evaluation datasets, fine-tune smaller judge models, and develop process-level reward signals for optimizing prompts, tool selection, planning, retrieval, and routing. Establish versioned scenarios and reliable measurement standards for agent performance in stateful enterprise environments.

Design, develop, and operate scalable VoIP infrastructure and telephony platforms supporting AI-driven voice workloads. Integrate SIP/RTP systems, LLMs, and real-time communications while improving observability, reliability, deployment, and SRE practices. Build tested, reusable software across telephony and AI integration layers, collaborate with product and infrastructure teams, support production systems, and mentor colleagues across telecom and AI engineering.

Develop and operate scalable VoIP infrastructure supporting AI-driven voice workloads. Build telephony platforms, observability features, and LLM integrations across SIP/RTP, Kamailio, RTPEngine, and related systems. Collaborate with engineering, product, infrastructure, and SRE teams on deployments, production support, automation, testing, and platform reliability. Mentor colleagues and promote engineering best practices across telecom and AI disciplines.

Lead hands-on development of AI and machine learning solutions for asset and wealth management. Design scalable data pipelines, develop and productionize ML and LLM models, implement inference services, and deploy platforms to AWS or Azure. Collaborate with business, product, and technical teams while driving architecture, innovation, security, and adoption of shared AI capabilities.

Build AI agent orchestration, routing, planning, memory, and action systems. Develop and deploy machine learning models for search ranking and proactive suggestions, integrate modern LLMs, and design evaluations to improve quality, speed, and cost. The role requires independent ownership, strong product sense, and collaboration in a fast-paced environment.

Designs, builds, deploys, and maintains production machine learning models and infrastructure. Responsibilities include developing ML applications, optimizing models and data pipelines, operating distributed systems and cloud-based platforms, automating testing and deployment, monitoring production models, and applying responsible AI, governance, and security practices. The role collaborates with Product, Data Science, and Agile engineering teams to deliver scalable ML solutions.

Develop and deploy production machine learning models and infrastructure at scale. Responsibilities include model development, data pipeline construction, cloud and Kubernetes operations, distributed data processing, CI/CD automation, model monitoring and retraining, responsible AI governance, and collaboration with product and data science teams. The role requires strong programming, machine learning, distributed systems, and production deployment expertise.