E2E AI SME Engineer

Univedge Consulting LLC; Santa Ana (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Univedge Consulting LLC in the United States is seeking an experienced E2E AI SME Engineer-cum-Product Owner to own the strategy, design, deployment, and continuous improvement of enterprise AI products. You will translate business needs into a prioritized roadmap and drive adoption and measurable value.

The role requires hands-on capability with LLMs, RAG, and agentic AI, plus strong product leadership to collaborate with business, data, and engineering teams across the organization.

Qualifications

• Experience delivering enterprise AI/ML products end to end.
• Hands-on with LLMs, RAG, and prompt engineering.
• Product ownership with roadmaps and Agile delivery.

Responsibilities

• Own end-to-end AI product lifecycle from discovery to adoption.
• Collaborate with stakeholders to define requirements and success metrics.
• Lead sprint planning, reviews, and release readiness.
• Build production-grade AI apps using Python, APIs, and cloud services.
• Establish MLOps, observability, and governance.

Skills

Python

SQL

LLMs

RAG

Prompt engineering

Embeddings

Vector search

LangChain

LlamaIndex

PyTorch

TensorFlow

Education

Bachelor’s or Master’s degree in CS/Engineering/AI/ML

Tools

Docker

Kubernetes

Git

LangChain

LlamaIndex

PyTorch

TensorFlow

Job description

Univedge Consulting is hiring for one of our direct clients - a global technology consulting leader based in the US. Immediate interviews for the below position.

Role : E2E AI SME Engineer-cum-Product Owner

Job Description:

Experienced, hands-on E2E AI Subject Matter Expert (SME) Engineer-cum-Product Owner to own the strategy, design, development, deployment, and continuous improvement of enterprise AI products. The ideal candidate combines deep expertise in machine learning, generative AI, large language models (LLMs), agentic AI, and retrieval-augmented generation (RAG) with strong product leadership. This role will translate business needs into a prioritized product roadmap, build production-grade solutions, and drive measurable adoption and business value from discovery through operations.

Key Responsibilities:

• Own the end-to-end AI product lifecycle, including discovery, vision, business case, roadmap, backlog, delivery, launch, adoption, and continuous improvement.
• Partner with business, data, engineering, security, legal, and operations stakeholders to define user journeys, functional and non-functional requirements, acceptance criteria, and measurable outcomes.
• Prioritize features and technical enablers based on customer value, feasibility, risk, cost, and strategic alignment; lead sprint planning, reviews, and release readiness.
• Design and build production-grade AI applications using Python, APIs, LLM frameworks, vector databases, and cloud-native services.
• Develop LLM, RAG, agentic AI, fine-tuning, prompt engineering, and tool-calling solutions for enterprise use cases.
• Create reference architectures, reusable components, technical prototypes, and coding standards while contributing directly to critical implementation work.
• Define data ingestion, chunking, embedding, retrieval, orchestration, and model-selection approaches across structured and unstructured data.
• Establish offline and online evaluation frameworks covering quality, relevance, groundedness, hallucination, latency, cost, safety, and business KPIs.
• Lead experimentation, model comparison, prompt and retrieval optimization, red-teaming, and human-in-the-loop validation.
• Deployment, MLOps/LLMOps & Operations:
• Lead secure deployment of AI services across development, test, and production environments using CI/CD, containers, infrastructure as code, and automated testing.
• Implement observability for model and application performance, data drift, prompt and response traces, token usage, latency, reliability, and cost.
• Own production readiness, incident response, root-cause analysis, release governance, vendor coordination, and service-level objectives.
• Responsible AI, Security & Governance:
• Embed privacy, security, responsible AI, explainability, access control, auditability, and regulatory requirements throughout the product lifecycle.
• Identify and mitigate risks related to sensitive data, prompt injection, data leakage, model misuse, bias, harmful output, and third-party models.
• Stakeholder Leadership & Adoption:
• Communicate product decisions, trade-offs, architecture, progress, risks, and value realization to technical and executive audiences.
• Drive pilots, user feedback, change management, training, adoption metrics, and scale-up plans; mentor engineers and promote AI engineering best practices.

Key Requirements:

• Education:
• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, AI/ML, or a related field, or equivalent practical experience.
• Experience:
• Proven experience delivering enterprise AI/ML products end to end, with direct ownership from problem discovery and solution design through production deployment and adoption.
• Strong hands-on experience building applications with LLMs, RAG, agentic workflows, prompt engineering, embeddings, vector search, and model evaluation.
• Demonstrated product ownership experience managing roadmaps, backlogs, user stories, acceptance criteria, stakeholder expectations, and Agile delivery.
• Experience integrating AI solutions with enterprise data platforms, APIs, business applications, and cloud ecosystems.
• Track record of deploying, monitoring, troubleshooting, and optimizing secure, scalable AI solutions in production.
• Technical Skills:
• Advanced Python and SQL skills, with experience in service development, REST APIs, testing, Git, and production-grade coding practices.
• Proficiency with AI/ML frameworks and LLM application tooling such as PyTorch or TensorFlow, Hugging Face, LangChain or LlamaIndex, and model-provider SDKs.
• Experience with vector databases, search technologies, data pipelines, orchestration, and structured and unstructured data processing.
• Working knowledge of AWS, Azure, or GCP; containers and Kubernetes; CI/CD; MLOps/LLMOps; observability; and infrastructure-as-code practices.
• Strong understanding of AI security, privacy, responsible AI, model risk, evaluation methods, and cost-performance optimization.
• Ability to convert ambiguous business problems into clear product requirements, solution architecture, prioritized delivery plans, and measurable outcomes.
• Excellent communication, facilitation, analytical thinking, customer empathy, decision-making, and cross-functional leadership skills.
• Ability to work hands-on while guiding multidisciplinary teams and influencing senior stakeholders without relying solely on formal authority.