Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A global professional services and technology organization is helping enterprises transform AI initiatives into production-scale business solutions. Through a combination of advanced engineering capabilities, industry expertise, and AI-driven innovation, the company delivers enterprise-grade solutions across software, data, artificial intelligence, cloud infrastructure, and digital transformation programs. Its engineering teams work closely with clients to modernize technology platforms and accelerate business outcomes through scalable AI implementations.
Key Responsibilities
Client Engagement
- Serve as a senior client-facing advisor and trusted engineering partner for product, data, and platform leaders.
- Lead executive-level discovery sessions and define success metrics related to quality, latency, cost, adoption, and risk.
- Develop phased implementation plans that guide projects from prototype through production deployment and scaling.
- Align executive sponsors, IT leaders, and business stakeholders around a shared AI vision.
- Participate in executive briefings, client pursuits, and strategic platform partner engagements.
Cross-Functional Pod Leadership & Program Governance
- Lead forward-deployed engineering pods consisting of onshore and offshore team members.
- Own delivery execution, resource planning, escalations, and overall project health.
- Establish and enforce delivery standards, sprint cadences, stakeholder communication plans, risk management practices, and quality controls.
- Coordinate multi-workstream and multi-team engagements to ensure consistent delivery and architecture standards.
- Mentor and develop engineers while fostering technical excellence and collaboration.
GenAI Solution Development
- Architect and oversee the delivery of LLM-powered applications, including copilots, AI assistants, agentic workflows, and knowledge search solutions.
- Define strategies for prompt engineering, tool integration, and human-in-the-loop workflows.
- Lead the design of Retrieval-Augmented Generation (RAG) pipelines, including ingestion, chunking, embeddings, vector retrieval, and hybrid search capabilities.
- Establish evaluation frameworks covering quality, hallucination risk, safety, latency, cost, and governance.
- Ensure AI solutions meet production-grade standards for scalability, reliability, and performance.
Required Qualifications
- Bachelor’s degree or equivalent in Computer Science, Data Science, Engineering, or a related field.
- 7+ years of experience in software engineering, data engineering, data science, or analytics engineering.
- 1+ year of hands-on experience building and deploying Generative AI or LLM-powered solutions in production environments.
- 1+ year of experience with Snowflake, including hands-on experience with one or more of the following:
- Cortex AI
- Cortex LLM Functions
- Cortex Agents
- Arctic Embed
- 1+ year of experience leading project workstreams and translating business challenges into AI-driven solutions.
- 1+ year of experience developing reliable, maintainable, and well-documented code.
- Ability to travel approximately 50% based on project and client requirements.
- Eligibility to work within applicable employment and immigration requirements.
Preferred Qualifications
- Experience with AWS, Azure, and/or Google Cloud environments and associated platform services.
- Demonstrated success working directly with client technical teams and business stakeholders in fast-paced environments.
- Experience with data engineering technologies such as Spark, Airflow, dbt, streaming platforms, and data modeling.
- Background in machine learning, data science, experimentation, feature engineering, or model evaluation.
- Experience with MLOps and LLMOps practices, including model monitoring, evaluation frameworks, and prompt management.
- Experience integrating LLM-based solutions with enterprise systems through APIs, microservices, or event-driven architectures.
- Experience working within hybrid onshore/offshore delivery models.
- Familiarity with security, privacy, governance, and compliance requirements in enterprise environments.
Disclaimer
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- No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
- No Client Relationship: This company is not a client of SpringCube unless stated.
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