Lead Applied AI Engineer – Enterprise Client Delivery

Lead Applied AI Engineer – Enterprise Client Delivery

Employer: Avihs AI Employment Type: Full-time Location: Remote / Hybrid, India Experience: 6–10 years Engagement: Long-term enterprise client assignment

ABOUT AVIHS AI

Avihs AI builds practical, production-ready artificial intelligence solutions for enterprises. We work across Generative AI, Document Intelligence, Computer Vision, multimodal systems, workflow automation, and AI-enabled business applications.

We are expanding our Applied AI team and looking for engineers who can take ownership beyond experimentation—understanding business problems, designing reliable systems, developing production software, measuring results, and supporting solutions after deployment.

ABOUT THE ROLE

We are hiring a Lead Applied AI Engineer for a dedicated, long-term engagement with one of our enterprise technology partners.

You will be a full-time employee of Avihs AI and work as an embedded member of the partner’s engineering, product, and customer-delivery teams. Avihs AI will remain responsible for your payroll, benefits, performance management, professional development, and long-term employment relationship.

Details about the partner organisation, reporting structure, and working model will be shared during the initial recruiter discussion.

This is a hands-on technical leadership role. You will be expected to convert complex and sometimes unclear business requirements into secure, scalable, measurable, and production-ready AI solutions.

WHAT YOU WILL OWN

• Discovery and technical definition of enterprise AI use cases. • End-to-end AI solution architecture. • Model selection, experimentation, development, and evaluation. • Production APIs, services, pipelines, and integrations. • Reliability, security, latency, quality, and cost measurement. • Production troubleshooting and continuous improvement. • Technical documentation and stakeholder communication. • Mentoring and technical guidance for other engineers.

KEY RESPONSIBILITIES

• Work with engineering, product, business, and customer teams to understand complex problems and define measurable AI solutions. • Determine whether a problem should be solved using Large Language Models, traditional machine learning, Computer Vision, Document AI, deterministic rules, or a combination of approaches. • Design systems covering data ingestion, preprocessing, retrieval, model execution, APIs, human review, monitoring, and feedback. • Develop enterprise applications involving areas such as: – Generative AI and Retrieval-Augmented Generation. – Document understanding and intelligent information extraction. – Computer Vision and multimodal AI. – Semantic search and enterprise knowledge assistants. – Anomaly detection and model monitoring. – AI-assisted workflow automation. • Build maintainable Python services and production APIs. • Create representative evaluation datasets and measurable acceptance criteria. • Evaluate AI systems for accuracy, reliability, hallucination risk, latency, security, and operating cost. • Investigate production failures, data-quality issues, model drift, unexpected outputs, and integration problems. • Design human-review and escalation workflows for uncertain or high-risk outputs. • Build reusable engineering components that can support multiple projects. • Participate in architecture reviews, code reviews, customer demonstrations, and technical planning. • Clearly explain assumptions, risks, limitations, and technical trade-offs. • Mentor engineers and support the transition of research or prototypes into production systems.

MINIMUM REQUIREMENTS

• Six or more years of professional experience in Applied AI, Machine Learning, Computer Vision, NLP, Document AI, or AI Product Engineering. • Personal ownership of at least two AI or machine-learning systems used in production or a real operational environment. • Strong technical depth in at least one of the following: – Generative AI and Large Language Model applications. – Computer Vision. – Document AI. – Machine-learning platforms and model reliability. • Working knowledge of at least one additional AI discipline. • Strong Python and software-engineering skills. • Experience developing APIs, services, pipelines, integrations, tests, and monitoring. • Experience working with incomplete, ambiguous, or changing requirements. • Understanding of model evaluation, hallucination management, data drift, security, and productio…