Robert Half
Our client is looking for a LEAD AI Engineer for their growing team.
The following information provides an overview of the skills, qualities, and qualifications needed for this role.
This is a full time role and not open to contract or C2C candidates.
About the Role Are you ready to shape the future of enterprise intelligence through pioneering AI and machine learning solutions? We are seeking an
Lead
AI/ML Lead Engineer
to lead the design and implementation of scalable, intelligent systems that solve complex business challenges. This role is central to transforming large-scale data into actionable insights through advanced machine learning, generative AI, and conversational technologies. As a technical leader, you will drive innovation across analytics, model engineering, and AI platforms, while collaborating closely with cross-functional teams to align solutions with strategic objectives.
What You’ll Do Business Analytics & Intelligence:
Analyze large and complex datasets to uncover trends, patterns, and insights using statistical and machine learning techniques. Model Engineering:
Design, train, deploy, and optimize machine learning models, classifiers, and algorithms for predictive analytics, anomaly detection, and optimization. Generative & Agentic AI:
Build and operationalize generative AI and agentic frameworks leveraging RAG pipelines, vector databases, and prompt orchestration techniques. Conversational AI Development:
Architect and fine-tune intelligent virtual assistants and multi-turn dialogue systems using large language models (LLMs), transformer architectures, and knowledge graphs. Knowledge Graph Integration:
Design and integrate semantic models and graph databases to enhance contextual understanding, reasoning, and information retrieval in AI systems. Enterprise Fine-Tuning:
Apply domain-specific LLM fine-tuning techniques (e.g., DPO, ORPO, SPIN) to align models with enterprise knowledge, policies, and workflows. AI Infrastructure & MLOps:
Develop and maintain robust, scalable ML pipelines using modern AI/ML Ops tools and best practices. Risk Mitigation & AI Safety:
Identify and address risks such as bias, model drift, and adversarial vulnerabilities; implement monitoring, governance, and safeguard strategies. Cross-Functional Collaboration:
Partner with data scientists, engineers, and business stakeholders to ensure AI solutions align with organizational goals. Stakeholder Engagement:
Deliver compelling demonstrations and recommendations, translating complex technical insights into clear, strategic guidance for business audiences. Research & Innovation:
Stay ahead of emerging trends in generative AI, AI safety, interpretability, and responsible AI practices.
What You’ll Need Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field. 5+ years of experience in AI/ML engineering, with strong proficiency in Python. Hands-on experience with machine learning libraries, predictive modeling, pattern recognition, and advanced analytics. Proven experience working with LLMs, NLP, and generative AI frameworks. Solid understanding of neural network architectures, including CNNs, RNNs, transformers, and attention mechanisms. Expertise in knowledge graph design and integration with conversational and retrieval-based AI systems. Familiarity with MLOps practices, model lifecycle management, and secure data governance. Experience with cloud platforms (Azure, AWS, GCP), containerization technologies (Docker, Kubernetes), and CI/CD pipelines. xsgimln Strong communication and leadership skills, with the ability to lead cross-functional initiatives and present effectively to senior and executive audiences.
The following information provides an overview of the skills, qualities, and qualifications needed for this role.
This is a full time role and not open to contract or C2C candidates.
About the Role Are you ready to shape the future of enterprise intelligence through pioneering AI and machine learning solutions? We are seeking an
Lead
AI/ML Lead Engineer
to lead the design and implementation of scalable, intelligent systems that solve complex business challenges. This role is central to transforming large-scale data into actionable insights through advanced machine learning, generative AI, and conversational technologies. As a technical leader, you will drive innovation across analytics, model engineering, and AI platforms, while collaborating closely with cross-functional teams to align solutions with strategic objectives.
What You’ll Do Business Analytics & Intelligence:
Analyze large and complex datasets to uncover trends, patterns, and insights using statistical and machine learning techniques. Model Engineering:
Design, train, deploy, and optimize machine learning models, classifiers, and algorithms for predictive analytics, anomaly detection, and optimization. Generative & Agentic AI:
Build and operationalize generative AI and agentic frameworks leveraging RAG pipelines, vector databases, and prompt orchestration techniques. Conversational AI Development:
Architect and fine-tune intelligent virtual assistants and multi-turn dialogue systems using large language models (LLMs), transformer architectures, and knowledge graphs. Knowledge Graph Integration:
Design and integrate semantic models and graph databases to enhance contextual understanding, reasoning, and information retrieval in AI systems. Enterprise Fine-Tuning:
Apply domain-specific LLM fine-tuning techniques (e.g., DPO, ORPO, SPIN) to align models with enterprise knowledge, policies, and workflows. AI Infrastructure & MLOps:
Develop and maintain robust, scalable ML pipelines using modern AI/ML Ops tools and best practices. Risk Mitigation & AI Safety:
Identify and address risks such as bias, model drift, and adversarial vulnerabilities; implement monitoring, governance, and safeguard strategies. Cross-Functional Collaboration:
Partner with data scientists, engineers, and business stakeholders to ensure AI solutions align with organizational goals. Stakeholder Engagement:
Deliver compelling demonstrations and recommendations, translating complex technical insights into clear, strategic guidance for business audiences. Research & Innovation:
Stay ahead of emerging trends in generative AI, AI safety, interpretability, and responsible AI practices.
What You’ll Need Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field. 5+ years of experience in AI/ML engineering, with strong proficiency in Python. Hands-on experience with machine learning libraries, predictive modeling, pattern recognition, and advanced analytics. Proven experience working with LLMs, NLP, and generative AI frameworks. Solid understanding of neural network architectures, including CNNs, RNNs, transformers, and attention mechanisms. Expertise in knowledge graph design and integration with conversational and retrieval-based AI systems. Familiarity with MLOps practices, model lifecycle management, and secure data governance. Experience with cloud platforms (Azure, AWS, GCP), containerization technologies (Docker, Kubernetes), and CI/CD pipelines. xsgimln Strong communication and leadership skills, with the ability to lead cross-functional initiatives and present effectively to senior and executive audiences.