We are a dynamic and rapidly expanding enterprise technology organization operating at the center of artificial intelligence, cloud software, automation, and data-driven product innovation. Our company builds and supports intelligent digital platforms that help organizations improve decision-making, automate complex workflows, personalize customer experiences, strengthen operational efficiency, and unlock measurable business value through advanced AI capabilities.
As artificial intelligence continues reshaping the way companies operate, compete, and serve customers, the need for strong AI engineering leadership has become mission-critical. Businesses are no longer looking only for experimental models or isolated prototypes. They need production-ready AI systems that are scalable, secure, explainable, reliable, measurable, and deeply integrated into real business workflows. The Lead AI Engineer role is designed for a technical leader who can bridge advanced machine learning, software engineering, cloud architecture, data infrastructure, and product execution.
Our organization is investing heavily in generative AI, large language models, machine learning platforms, AI agents, retrieval-augmented generation, predictive analytics, intelligent automation, model evaluation, responsible AI practices, data pipelines, MLOps, and enterprise-grade AI application development. We are seeking a highly capable Lead AI Engineer who can guide technical direction, build advanced AI solutions, mentor engineers, partner with product leaders, and help move AI initiatives from concept into reliable production systems.
This is not a traditional engineering role focused only on model experimentation or backend development. The Lead AI Engineer will serve as a hands-on technical leader responsible for designing AI architectures, building machine learning systems, integrating models into customer-facing products, improving model performance, supporting AI infrastructure, guiding technical standards, and ensuring AI solutions solve meaningful business problems.
The selected candidate will work closely with Software Engineering, Data Science, Machine Learning, Product, Data Engineering, Cloud Infrastructure, Security, DevOps, Customer Success, Business Operations, Legal, Compliance, and Executive Leadership to ensure AI solutions are technically strong, commercially useful, secure, scalable, and aligned with enterprise priorities.
This is a strong opportunity for an experienced AI engineer who wants to lead meaningful artificial intelligence work, influence product direction, support enterprise-scale systems, and contribute to a remote-first organization where AI is treated as a core driver of product differentiation, customer value, and long-term growth.
As Lead AI Engineer, you will design, develop, deploy, and improve AI-powered systems that support product innovation, customer automation, predictive intelligence, workflow optimization, and enterprise decision-making.
You will be responsible for leading AI engineering initiatives, building scalable model pipelines, integrating machine learning and generative AI capabilities into software products, improving system reliability, and mentoring engineers across AI and software development practices.
You will serve as a trusted technical leader while maintaining accountability for architecture quality, model performance, production readiness, code quality, documentation, security alignment, and measurable business impact.
The ideal candidate combines strong AI engineering experience, machine learning depth, software engineering discipline, cloud architecture knowledge, product awareness, and leadership ability to build AI systems that work reliably in real enterprise environments.
Lead the design, development, deployment, and optimization of AI-powered applications, machine learning systems, generative AI features, and intelligent automation solutions.
Build production-grade AI systems using large language models, machine learning models, retrieval-augmented generation, embeddings, vector databases, model APIs, and cloud-native services.
Develop and maintain AI pipelines for data ingestion, feature engineering, model training, model evaluation, prompt management, inference, monitoring, and continuous improvement.
Partner with Product and Engineering teams to translate business problems into AI solutions that are practical, scalable, measurable, and aligned with customer needs.
Lead architecture decisions for AI services, model integrations, backend systems, APIs, data pipelines, orchestration workflows, and cloud infrastructure.
Evaluate, fine-tune, and integrate AI models from open-source frameworks, commercial providers, internal model libraries, and cloud AI platforms.
Design and improve retrieval systems, semantic search, ranking logic, knowledge bases, AI agents, workflow automation, recommendation systems, classification models, and predictive analytics capabilities.
Build model evaluation frameworks to measure accuracy, relevance, latency, reliability, hallucination risk, bias, safety, cost, and business effectiveness.
Implement MLOps and LLMOps practices, including experiment tracking, model versioning, deployment automation, monitoring, rollback planning, alerting, documentation, and governance.
Partner with Data Engineering teams to ensure AI systems have access to reliable, well-governed, high-quality data sources and scalable data infrastructure.
Partner with Security, Legal, and Compliance teams to support responsible AI practices, data privacy, access controls, auditability, model risk management, and secure deployment.
Mentor engineers and technical team members by reviewing designs, improving code quality, sharing AI best practices, and supporting technical decision-making.
Troubleshoot AI system issues, including model drift, data quality problems, latency bottlenecks, inference failures, prompt instability, pipeline errors, integration defects, and production incidents.
Stay current with AI engineering trends, model capabilities, emerging frameworks, cloud AI services, responsible AI standards, and practical enterprise adoption patterns.
7+ years of experience in software engineering, machine learning engineering, AI engineering, data science engineering, backend development, applied ML, or advanced analytics technology.
3+ years of hands-on experience building, deploying, or integrating machine learning, generative AI, natural language processing, recommendation systems, predictive models, or AI-powered software features.
Experience working within enterprise SaaS, AI-enabled technology, cloud platforms, fintech, healthcare technology, cybersecurity, ecommerce, customer experience technology, automation platforms, or data intelligence environments preferred.
Strong programming experience using Python, TypeScript, JavaScript, Java, Scala, Go, C++, or similar languages, with strong software engineering fundamentals.
Experience with AI and machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, LlamaIndex, MLflow, Ray, Spark ML, Keras, or similar technologies.
Experience with large language models, embeddings, vector databases, prompt engineering, retrieval-augmented generation, AI agents, model evaluation, fine-tuning, or inference optimization.
Experience with cloud platforms such as AWS, Azure, or Google Cloud, including AI services, compute, storage, networking, serverless architecture, containers, and managed data platforms.
Familiarity with databases and data platforms such as PostgreSQL, MongoDB, Redis, Snowflake, BigQuery, Redshift, Databricks, Elasticsearch, Pinecone, Weaviate, Milvus, Chroma, or similar technologies.
Experience with MLOps or DevOps practices, including CI/CD, Docker, Kubernetes, model deployment, monitoring, logging, observability, version control, and automated testing.
Strong understanding of APIs, microservices, backend systems, distributed systems, data pipelines, model serving, latency optimization, security, and production reliability.
Ability to lead technical discussions, mentor engineers, review architecture, influence standards, and communicate complex AI concepts clearly to technical and non-technical stakeholders.
Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, Mathematics, Statistics, or a related technical field preferred.
Master’s degree, PhD, AI certification, cloud certification, machine learning specialization, or equivalent advanced practical experience preferred.
Competitive compensation package.
Comprehensive medical, dental, and vision healthcare coverage.
Flexible remote-first work environment.
Performance bonus eligibility.
AI innovation and product delivery incentive opportunities.
Long-term incentive opportunities where applicable.
Retirement savings plan with company contribution.
Professional development and continuing education reimbursement.
Artificial intelligence, machine learning, generative AI, cloud architecture, MLOps, LLMOps, data engineering, responsible AI, and technical leadership training resources.
Wellness and mental health support programs.
Paid time off and company holidays.
Opportunity to support enterprise-level AI product development, intelligent automation, advanced analytics, scalable machine learning systems, and digital transformation initiatives.
Access to modern AI frameworks, cloud platforms, vector databases, model providers, data platforms, monitoring tools, collaboration systems, and AI-enabled engineering resources.
Strong technical leader with the ability to guide AI architecture, engineering execution, model integration, and production readiness.
Hands-on builder who can move from concept to prototype to scalable production system with discipline and practical judgment.
Deeply analytical and technically curious, with the ability to evaluate model performance, system behavior, business value, and technical tradeoffs.
Product-minded and customer-focused, with the ability to connect AI capabilities to real user needs, workflow improvements, and measurable outcomes.
Strong communicator who can explain AI systems, model limitations, architecture choices, risks, and recommendations clearly to technical and non-technical audiences.
Collaborative and able to work effectively with engineering teams, data scientists, product leaders, security partners, infrastructure teams, and business stakeholders.
Quality-focused and reliable, with strong ownership of code quality, testing, documentation, monitoring, model evaluation, and operational stability.
Responsible AI-minded, with awareness of privacy, security, fairness, explainability, data governance, hallucination risk, and model safety considerations.
Calm and effective under pressure, especially during production incidents, model failures, urgent releases, customer-impacting issues, or executive-level AI priorities.
High integrity and discretion when handling customer data, model inputs, proprietary algorithms, confidential business logic, production systems, and sensitive technical documentation.
Support executive and product leadership with AI strategy, technical feasibility, model selection, architecture planning, delivery estimates, and innovation opportunities.
Help improve product differentiation by building AI-powered features that increase automation, personalization, intelligence, speed, and customer value.
Support digital transformation by integrating AI into workflows, business systems, customer experiences, reporting tools, and operational processes.
Align AI engineering work with company goals, customer needs, security standards, compliance expectations, scalability requirements, and product roadmaps.
Strengthen engineering maturity by improving MLOps practices, deployment standards, monitoring, testing, documentation, and model evaluation frameworks.
Partner with Data Engineering to improve data availability, data quality, feature pipelines, retrieval systems, and analytics readiness for AI applications.
Partner with Security and Compliance teams to support privacy reviews, access control, data retention, model risk assessment, governance, and responsible AI practices.
Partner with Customer Success and Operations teams to identify high-impact AI use cases, automation opportunities, workflow bottlenecks, and customer value drivers.
Support technical talent development by mentoring engineers, sharing best practices, improving code review standards, and strengthening AI engineering capability across teams.
Help turn artificial intelligence into a strategic business capability that improves customer outcomes, operational efficiency, product value, and long-term enterprise growth.
Remote-first AI engineering leadership role.
Periodic travel may be required for engineering offsites, AI strategy workshops, product planning sessions, customer innovation meetings, leadership reviews, security reviews, or company gatherings.
High-visibility technical role supporting AI product development, machine learning systems, generative AI capabilities, intelligent automation, and enterprise technology strategy.
Fast-paced environment focused on innovation, reliability, security, scalability, model quality, customer value, and responsible AI execution.
Regular collaboration with Software Engineering, Data Science, Machine Learning, Product, Data Engineering, Cloud Infrastructure, Security, DevOps, Customer Success, Business Operations, Legal, Compliance, and Executive Leadership.
Opportunity to influence AI architecture, product intelligence, model performance, engineering standards, platform scalability, and long-term technology direction.
Requires flexibility during product launches, AI experiments, customer escalations, production incidents, model evaluations, security reviews, data quality issues, and strategic AI initiatives.
Role requires handling confidential customer data, proprietary models, internal algorithms, product strategy, system architecture, source code, business logic, and sensitive technical materials with discretion.
AI Engineering Leadership.
Machine Learning Engineering.
Generative AI Development.
LLM Application Engineering.
Applied Machine Learning.
AI Product Development.
MLOps and LLMOps.
Cloud AI Architecture.
Model Evaluation.
AI System Integration.
Backend AI Services.
Data Pipeline Support.
Responsible AI Implementation.
Technical Mentorship.
Remote AI Engineering Leadership.
Compensation Package: $280,000 – $395,000
Base Salary: $280,000 – $395,000.
Annual Performance Bonus.
AI Innovation Incentives.
Product Delivery and Engineering Performance Incentives.
Model Performance and Platform Reliability Incentives.
Long-Term Incentive Eligibility where applicable.
Additional benefits may include:
• Comprehensive healthcare coverage.
• Retirement savings plan with company contribution.
• Wellness and mental health programs.
• Professional education assistance.
• Flexible remote work environment.
• Paid time off and company holidays.
• Artificial intelligence, machine learning, generative AI, LLMOps, MLOps, cloud architecture, responsible AI, data engineering, and technical leadership training.
• Access to modern AI frameworks, model providers, vector databases, cloud platforms, data systems, monitoring tools, collaboration platforms, and AI-enabled engineering resources.
We are committed to fostering a workplace where innovation, diversity, and inclusion drive meaningful business outcomes. We provide equal employment opportunities to all applicants and employees regardless of race, religion, gender, age, disability, veteran status, sexual orientation, or any other protected characteristic under applicable law.
Lead high-impact AI engineering initiatives that shape intelligent products, automation systems, customer experiences, and enterprise technology strategy.
Work in a remote-first organization using modern AI frameworks, cloud platforms, data systems, model providers, and AI-enabled engineering tools.
Partner with product, engineering, data, security, customer success, and leadership teams on meaningful artificial intelligence initiatives.
Contribute to a company investing heavily in generative AI, machine learning systems, intelligent automation, scalable cloud platforms, and responsible AI practices.
Build production-grade AI solutions that create measurable value rather than remaining limited to research concepts or isolated prototypes.
Build a strong AI engineering leadership platform by turning technical expertise, product judgment, and innovation discipline into measurable enterprise value.
At Admiral Talent, our vision is to redefine staffing through genuine partnerships, innovation, and a people-first mindset. Guided by experienced leadership, we’re committed to driving growth, empowering talent, and delivering exceptional results for every client we serve.