AI​/ML Engineering

Job-ID Reference
26-14386

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 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 Responsibilities 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 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 & Delivery Lead AI/ML initiatives from ideation experimentation MVP production scale.

Establish processes for rapidly validating AI use cases while maintaining…