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 consulting organization is helping enterprises accelerate AI adoption through advanced engineering, data, and cloud solutions. The company works with organizations across industries to design, deploy, and scale Generative AI solutions that drive measurable business impact. By combining deep technical expertise with industry-focused delivery models, the organization enables clients to modernize operations and unlock new opportunities through AI innovation.
Key Responsibilities
Client Engagement
- Serve as the senior client-facing engineering leader, building trusted relationships with product, data, and platform stakeholders.
- Lead executive discovery sessions and define success metrics including quality, latency, cost, adoption, and risk.
- Develop phased roadmaps that guide clients from prototype development to production deployment and scaling.
- Align executive sponsors, IT leaders, and business stakeholders around AI transformation strategies.
- Support business development activities, executive briefings, and strategic platform partner engagements.
Cross-Functional Leadership & Program Governance
- Lead engineering teams responsible for delivering GenAI solutions across client engagements.
- Oversee execution, resource planning, issue escalation, and overall delivery performance.
- Establish and maintain delivery standards including sprint planning, stakeholder communication, risk management, and quality assurance processes.
- Coordinate complex, multi-workstream initiatives to ensure consistent architecture and client outcomes.
- Mentor and develop engineering talent while fostering technical excellence and collaboration.
Generative AI Solution Development
- Architect and oversee the delivery of AI-powered applications such as copilots, intelligent assistants, agentic workflows, and enterprise knowledge search solutions.
- Define best practices for prompt engineering, tool integration, and human-in-the-loop workflows.
- Lead the design and governance of Retrieval-Augmented Generation (RAG) pipelines, including ingestion, chunking, embeddings, vector retrieval, and hybrid search architectures.
- Establish evaluation frameworks that measure quality, hallucination risk, safety, latency, cost, and governance compliance.
- Ensure solutions meet enterprise requirements for scalability, reliability, and performance.
Engineering & Data Foundations
- Review and contribute to production-quality code and system architecture decisions.
- Guide the development of data pipelines that support GenAI applications and workflows.
- Promote best practices in testing, CI/CD, logging, monitoring, version control, and documentation.
- Support cloud-native architectures across AWS, Microsoft Azure, and Google Cloud environments.
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 GenAI or LLM-powered solutions in production or client environments.
- 1+ year of experience working with leading GenAI platforms such as Anthropic, Google, or OpenAI.
- Experience with technologies including Claude API, Claude for Enterprise, Claude Code, Gemini API, Vertex AI Agent Builder, GPT-4o, Assistants API, Responses API, or OpenAI Agents SDK.
- 1+ year of experience leading project workstreams and translating business challenges into AI-powered solutions.
- Experience building reliable, maintainable, and well-documented software systems.
- Ability to travel up to 50% based on client and project requirements.
Preferred Qualifications
- Experience working with cloud platforms including AWS, Azure, and Google Cloud.
- Proven ability to collaborate directly with client technical teams and business stakeholders in fast-paced environments.
- Experience with Spark, Airflow, dbt, streaming technologies, data modeling, machine learning, or data science workflows.
- Familiarity with MLOps and LLMOps practices, including evaluation frameworks, monitoring, and prompt management.
- Experience integrating LLM solutions with enterprise systems through APIs, microservices, or event-driven architectures.
- Experience working within hybrid onshore/offshore delivery teams.
- Knowledge of security, privacy, governance, and compliance requirements for enterprise AI solutions.
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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