About this role.
Actian is seeking an AI Enablement Lead to drive the internal AI transformation and embed AI capabilities into their Data Intelligence Platform. This role involves architecting scalable AI tooling, establishing LLMOps and governance frameworks, and optimizing cost and latency. The ideal candidate has deep technical expertise in Python, vector databases, and orchestration frameworks like LangChain, and possesses a high-agency mindset to proactively solve bottlenecks. They will collaborate with engineering teams to deploy production-ready AI features and upskill cross-functional teams.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
4/5Pace & Pressure
4/5Autonomy Level
5/5Communication Load
4/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
Skills and capabilities most closely associated with this opportunity.
Cover letter sample
Dear Hiring Manager,
I am excited to apply for the AI Enablement Lead position at Actian. With over 8 years of experience in AI/ML engineering and a strong background in building scalable platforms, I am confident in my ability to drive your AI transformation. I have successfully architected LLMOps pipelines and integrated generative AI features into enterprise products, reducing time-to-market by 30%.
At my previous role, I led the development of a centralized AI orchestration layer that enabled multiple teams to deploy AI models securely and efficiently. I am passionate about democratizing AI and ensuring responsible governance. I look forward to bringing my technical vision and collaborative leadership to Actian.
Sincerely,
[Your Name]
Sample interview questions
I would start by identifying common AI use cases across teams, then build a centralized API gateway with standardized interfaces for model inference, vector search, and data ingestion. Use microservices architecture with Kubernetes for scalability, and implement caching and load balancing. Incorporate monitoring for cost and latency, and provide SDKs for easy integration.
I have implemented guardrails for model outputs using content filtering and bias detection. I set up evaluation pipelines with predefined datasets to test model performance, accuracy, and fairness. Monitoring tools like Weights & Biases track model drift, and automated rollback is triggered if quality drops. I also enforce data privacy by masking PII before sending to LLMs.
I evaluate factors like cost, latency, accuracy, customization needs, and data privacy. For sensitive data, open-source models deployed on-premises are preferred. For cutting-edge performance, commercial APIs like GPT-4 might be used. I often start with a proof-of-concept comparing both, measuring throughput and quality metrics before deciding.
At my last company, I led a team to implement a retrieval-augmented generation (RAG) system for customer support. We started with a quick prototype using LangChain and Pinecone. After validation, I worked with product and engineering to containerize the solution, set up CI/CD pipelines, and integrate with their existing chat infrastructure. We also trained the support team on the new tool.
I use techniques like prompt compression, caching common queries, and batching requests. I also choose the smallest model that meets performance requirements. Monitoring token usage per user and setting limits helps control costs. Sometimes, I implement semantic caching to avoid redundant API calls for similar questions.
The AI Enablement Team is the catalyst for internal transformation and product acceleration across Actian. In the modern data landscape, AI is not a siloed experimental lab; it is a core capability that must be embedded into our product DNA and our engineering workflows.
We are looking for an AI Enablement Lead who will architect, scale, and own our AI enablement strategy end-to-end. You are not a theoretical researcher or a passive prompt engineer; you are a highly practical, technical driver who builds the foundational platforms, tooling, and frameworks that allow other product and engineering teams to deploy AI safely, rapidly, and at scale. You will democratize AI across the organization, establish modern LLMOps/MLOps practices, and directly impact the Actian Data Intelligence Platform by introducing agentic workflows, intelligent data pipelines, and cutting-edge capabilities.
Core Responsibilities:
Internal AI tooling: Design and maintain the core AI orchestration layers, centralized API gateways, and reusable frameworks (e.g., advanced RAG architectures, agentic frameworks) for company-wide consumption.
Product AI Integration: Collaborate directly with core engineering teams to embed production-ready generative AI and machine learning features into the Actian Data Intelligence Platform.
LLMOps & Governance Infrastructure: Establish strict guardrails, evaluation frameworks, and monitoring tools to track model performance, bias, data privacy, and security across all AI implementations.
Cost & Latency Optimization: Actively monitor and manage cloud and API compute spend (token management, open-source vs. commercial models) and optimize execution latency for production AI features.
Cross-Functional Upskilling: Lead workshops, design blueprints, and create documentation to empower non-AI engineering teams to build and maintain their own AI-driven features confidently.
Rapid Prototyping (PoC to Production): Drive the engineering execution of high-impact AI proof-of-concepts, ensuring they are built with production-grade code that scales seamlessly.
Standardization of Tooling: Define and enforce the organization’s official AI stack, from vector database selection and vector embeddings strategies to semantic caching mechanisms.
Vendor & Open-Source Strategy: Evaluate and manage partnerships with AI model providers and lead the technical assessment of cutting-edge open-source models to keep Actian at the vanguard of innovation.
Data-Driven Impact Tracking: Define and track operational metrics for the AI Enablement function, such as developer adoption rates, reduction in time-to-market for AI features, and ROI of implemented AI tools.
Qualifications & Profile:
Technical Background: Strong background as a Senior AI/ML Engineer, LLMOps Engineer, or Software Architect who has successfully built and scaled AI-powered applications in enterprise SaaS or complex data platforms.
AI & Engineering Mastery: Deep technical expertise in Python or Go, semantic search, vector databases (e.g., Pinecone, Milvus, pgvector), orchestration frameworks (LangChain, LlamaIndex), and fine-tuning or prompt engineering of state-of-the-art Large Language Models (LLMs).
Extreme Ownership: High-agency mindset. You don’t wait for product teams to ask for AI capabilities; you proactively build the frameworks that solve their bottlenecks before they even identify them.
Software Engineering Rigor: You treat AI development as software engineering. You understand CI/CD, unit testing for AI (evaluation datasets), containerization (Docker/Kubernetes), and clean code architecture.
Influence Without Authority: Exceptional leadership and communication skills. You can inspire and align disparate engineering teams around a shared technical vision without being their direct line manager.
Communication: Exceptional verbal and written English communication skills. Ability to demystify complex AI anomalies or architectures into clear business value for internal stakeholders and executives.
What We Offer:
- The chance to be part of an innovative, fast-growing company making a significant impact in the data management space.
- Collaboration with a passionate and diverse team.
- Competitive salary and benefits package.
- Flexible work arrangements (remote or hybrid).
- Opportunities for professional growth and development.
Annual salary information is not provided for this position. Explore salary ranges for similar roles in our Salary Directory ›
Upload your resume now
- To unlock remote work opportunities and be discovered by global employers.
This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.
