AIML Engineering

Ampcus 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 organizations 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.

• AI/ML Strategy & Roadmap
  • Define 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 Engineering
  • Lead 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 Learning
  • Lead 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 & Architecture
  • Define 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 AI
  • Establish 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 & Delivery
  • Lead 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 & Leadership
  • Drive 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.

• 10 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.

• Large Language Models / Foundation Models
• Generative AI
• AI Agents / Agentic AI
• RAG / Retrieval Systems
• Prompt Engineering
• Embeddings & Vector Databases
• Model Fine-tuning
• LLM Evaluation
• Machine Learning & Deep Learning
• MLOps
• Python
• PyTorch / TensorFlow
• LangChain / LangGraph or equivalent frameworks
• Model APIs and AI platforms
• AWS / Azure / GCP
• Kubernetes / Docker
• APIs & Microservices
• Data Engineering & Data Pipelines
• AI Security & Responsible AI

• Number and quality of AI/ML use cases successfully moved into production
• Business value and ROI generated through AI initiatives
• AI adoption across engineering and business teams
• Improvement in software engineering productivity
• Reduction in development and testing cycle time
• Model accuracy reliability and performance
• LLM quality and evaluation scores
• AI application latency and infrastructure cost
• Reduction in manual processes through intelligent automation
• Adoption and effectiveness of AI developer tools
• Compliance with AI security governance and responsible AI standards

The 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.