AI/ML Engineering

Job-ID32942203Reference26-14386Ampcus Inc. is a certified global provider of a broad range of Technology and Business consulting services. We are in search of a highly motivated candidate to join our talented Team.We are seeking a strategic and technically strong leader to define and execute the organization’s AI/ML strategy and accelerate the adoption of artificial intelligence across products, engineering, and business functions. This role will lead the design, development, evaluation, and productionization of AI/ML solutions, with a strong focus on Generative AI, LLMs, AI agents, machine learning, intelligent automation, and AI-enabled software engineering. The ideal candidate combines deep technical expertise with strong business and organizational leadership skills and can translate emerging AI capabilities into scalable, secure, measurable business outcomes.Key ResponsibilitiesAI/ML Strategy & RoadmapDefine and execute the enterprise AI/ML strategy aligned with business and technology objectives.Identify and prioritize high-value AI/ML use cases across products, engineering, and business operations.Develop AI/ML roadmaps covering experimentation, adoption, productionization, and scale.Evaluate emerging AI/ML technologies, models, platforms, and frameworks.Establish standards and best practices for AI/ML development, deployment, evaluation, and governance.Generative AI & LLM EngineeringLead development and implementation of Generative AI solutions using LLMs and multimodal models.Design and implement RAG, agentic AI, tool use, function calling, and multi-agent architectures.Develop LLM-powered applications and intelligent workflows.Evaluate and select foundation models based on quality, latency, cost, security, and business requirements.Establish LLM evaluation frameworks covering accuracy, relevance, hallucination, safety, latency, and cost.Drive adoption of AI coding assistants and AI-powered software development practices.Identify opportunities to use AI to improve developer productivity, testing, quality, and engineering efficiency.Machine LearningLead development and deployment of machine learning models across business and product use cases.Define ML modeling, experimentation, training, validation, and deployment strategies.Establish MLOps practices for model lifecycle management, monitoring, retraining, and governance.Partner with Data Science and Data Engineering teams to build reliable ML pipelines and data platforms.Apply statistical modeling, predictive analytics, classification, recommendation, anomaly detection, and optimization techniques where appropriate.AI Engineering & ArchitectureDefine scalable architectures for AI/ML applications and platforms.Design AI systems integrating models, data, APIs, enterprise applications, and business workflows.Establish patterns for model serving, inference, prompt management, vector databases, embeddings, and retrieval systems.Design for scalability, reliability, observability, performance, and cost optimization.Integrate AI/ML capabilities into existing enterprise technology ecosystems.AI Governance, Security & Responsible AIEstablish AI governance, security, privacy, and responsible AI practices.Partner with cybersecurity, legal, compliance, data governance, and architecture teams.Define controls for sensitive data, model access, prompt security, AI output validation, and model risks.Establish AI risk assessment and approval processes for production deployments.Ensure AI/ML solutions meet enterprise security, regulatory, and ethical requirements.AI Productization & DeliveryLead AI/ML initiatives from ideation experimentation MVP production scale.Establish processes for rapidly validating AI use cases while maintaining production engineering standards.Define KPIs and success metrics for AI initiatives.Measure business value, productivity improvements, quality improvements, cost savings, and adoption.Work closely with Product, Engineering, Data, UX, Security, and business stakeholders to deliver AI-powered products and capabilities.AI Transformation & LeadershipDrive organizational adoption of AI/ML technologies.Establish AI Centers of Excellence, communities of practice, or enablement programs.Educate engineering and business teams on effective AI adoption.Mentor AI/ML engineers, architects, data scientists, and technical leaders.Build and scale high-performing AI/ML engineering teams.Partner with executive leadership to communicate AI opportunities, risks, investments, and outcomes.Required Qualifications10 years of experience in software engineering, machine learning, data science, AI engineering, or technology leadership.5 years of hands-on experience with AI/ML technologies.Demonstrated experience taking AI/ML solutions from experimentation to production.Strong experience with Generative AI and Large Language Models (LLMs).Experience designing RAG and agentic AI architectures.Strong understanding of machine learning fundamentals and model lifecycle management.Experience with cloud AI/ML platforms such as AWS, Azure, or GCP.Experience with Python and modern AI/ML frameworks.Strong understanding of APIs, distributed systems, data architectures, and cloud-native technologies.Experience with AI/ML evaluation, monitoring, observability, and governance.Excellent communication and executive stakeholder-management skills.Preferred Technical SkillsLarge Language Models / Foundation ModelsGenerative AIAI Agents / Agentic AIRAG / Retrieval SystemsPrompt EngineeringEmbeddings & Vector DatabasesModel Fine-tuningLLM EvaluationMachine Learning & Deep LearningMLOpsPythonPyTorch / TensorFlowLangChain / LangGraph or equivalent frameworksModel APIs and AI platformsAWS / Azure / GCPKubernetes / DockerAPIs & MicroservicesData Engineering & Data PipelinesAI Security & Responsible AIKey Performance IndicatorsNumber and quality of AI/ML use cases successfully moved into productionBusiness value and ROI generated through AI initiativesAI adoption across engineering and business teamsImprovement in software engineering productivityReduction in development and testing cycle timeModel accuracy, reliability, and performanceLLM quality and evaluation scoresAI application latency and infrastructure costReduction in manual processes through intelligent automationAdoption and effectiveness of AI developer toolsCompliance with AI security, governance, and responsible AI standardsLeadership ProfileThe successful candidate is a hands-on AI/ML technology leader who can operate across strategy, architecture, engineering, and execution. They should be comfortable moving between discussions with executive leadership and hands-on technical teams, translating business problems into AI opportunities and then guiding teams through the implementation of scalable, production-grade AI/ML solutions.Ampcus is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veterans or individuals with disabilities.