[DT] : AI Engineering / Lead
Role Overview
As a Forward Deployed AI Engineer, bridge client problem spaces and enterprise AI system architecture.
Design, build, deploy, and scale custom AI solutions in client cloud or on-premises environments.
Role scope is adapted by experience: intermediate technical execution or senior/team-lead client delivery.
Key Skills for This Role
Full Job Posting
Role overview
As a Forward Deployed AI Engineer, bridge client problem spaces and enterprise AI system architecture.
Design, build, deploy, and scale custom AI solutions in client cloud or on-premises environments.
Role scope is adapted by experience: intermediate technical execution or senior/team-lead client delivery.
Client delivery
- Translate client engineering and business requirements into technical specifications and AI workflows.
- Deploy RAG pipelines, fine-tuned models, vector databases, and agent frameworks in client environments.
- Integrate AI microservices with enterprise infrastructure, legacy data stores, APIs, and business systems.
- At senior or lead level, advise client CTOs and engineering leaders on AI strategy, security, and ROI.
AI engineering and architecture
- Design data orchestration pipelines, vector search infrastructure, and framework integrations.
- Optimize latency, throughput, token usage, and computing costs for enterprise workloads.
- Implement evaluation, observability, and safety frameworks for accuracy, drift, hallucinations, and security.
- At senior or lead level, design distributed systems meeting compliance and regulatory standards such as HIPAA, GDPR, and SOC 2.
Delivery leadership and quality
- Establish clean codebases, automated testing, CI/CD pipelines, and MLOps or LLMOps practices.
- Troubleshoot performance bottlenecks, system outages, and deployment issues in customer environments.
- Conduct technical enablement and knowledge transfer for client engineering teams.
- At senior or lead level, oversee delivery teams, mentor engineers, conduct code reviews, and create reusable playbooks.
Required qualifications
- Have 3–8+ years in software design, solution engineering, or systems architecture.
- Have proven experience building and deploying production enterprise AI/ML solutions.
- Be fluent in Python and modern software engineering practices.
- Have expertise with PyTorch or TensorFlow, Hugging Face, OpenAI or Anthropic APIs, vector databases, prompt engineering, and agent frameworks.
- Have hands-on experience with cloud providers, Docker, CI/CD, and infrastructure concepts.
- Communicate technical AI trade-offs effectively to non-technical business leaders.
Preferred qualifications
- TypeScript/Node.js or Go is a plus.
- Kubernetes or Terraform is preferred for senior candidates.
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