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 leading global professional services organization is expanding its AI engineering practice to help enterprises transform ambitious AI initiatives into production-ready, enterprise-scale solutions. Through a combination of engineering excellence, industry expertise, and collaborative delivery models, the organization partners with clients to build, deploy, and optimize advanced Generative AI applications that drive meaningful business outcomes across industries.
Key Responsibilities
- Lead forward-deployed engineering teams delivering enterprise-scale Generative AI solutions for strategic clients.
- Establish technical direction while remaining actively involved in system architecture, code reviews, debugging, and solution implementation.
- Build trusted relationships with client executives, product leaders, data teams, and platform stakeholders.
- Lead executive discovery sessions and define project success metrics, including quality, latency, adoption, cost, governance, and risk management.
- Translate business requirements into scalable AI solutions and guide clients from prototype through production deployment.
- Coordinate engineering pods across onshore and offshore teams to ensure successful project execution.
- Manage sprint planning, delivery standards, stakeholder communication, risk management, and overall delivery health.
- Mentor and develop engineering team members while promoting engineering excellence and continuous learning.
- Architect and oversee the development of AI-powered copilots, assistants, agentic workflows, and knowledge search applications.
- Define prompt engineering strategies, tool integration approaches, and human-in-the-loop workflows.
- Design and govern Retrieval-Augmented Generation (RAG) pipelines, including data ingestion, chunking, embeddings, vector retrieval, and hybrid search.
- Establish evaluation frameworks covering AI quality, hallucination risk, latency, governance, safety, and operational performance.
- Review production-quality code and guide software architecture decisions.
- Lead the design of scalable data pipelines supporting enterprise AI workloads.
- Promote best practices for testing, CI/CD, documentation, logging, version control, and data governance.
- Collaborate with cross-functional teams to deliver secure, reliable, and scalable cloud-based AI solutions.
Required Qualifications
- Bachelor’s degree (or equivalent) in Computer Science, Data Science, Engineering, or a related technical 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 or client environments.
- 1+ year of experience with Snowflake, including hands-on expertise with one or more of the following technologies:
- Cortex AI
- Cortex LLM Functions
- Cortex Agents
- Arctic Embed
- 1+ year of experience leading technical workstreams or client engagements.
- Experience translating business challenges into AI-powered technical solutions.
- Proven ability to develop reliable, maintainable, and well-documented software.
- Willingness to travel approximately 50% based on project and client requirements.
- Eligibility to work within applicable employment authorization requirements.
Preferred Qualifications
- Experience working with AWS, Microsoft Azure, and/or Google Cloud Platform.
- Demonstrated experience collaborating directly with client engineering teams and business stakeholders.
- Strong background in data engineering technologies such as Spark, Airflow, dbt, streaming platforms, or data modeling.
- Experience with machine learning, feature engineering, experimentation, or model evaluation.
- Familiarity with MLOps and LLMOps practices, including model monitoring, prompt management, and evaluation frameworks.
- Experience integrating LLM applications with enterprise systems using APIs, microservices, or event-driven architectures.
- Experience leading hybrid onshore/offshore engineering teams.
- Understanding of enterprise security, privacy, governance, and compliance best practices.
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