[DT] : AI Engineering / Lead

Job Family: AI Engineering / Lead

Level: Intermediate to Senior / Team Lead (Positioning and scope adapted based on experience)

Experience Guideline: 3-8+ years in software and systems engineering, with strong hands-on enterprise AI delivery and team/delivery leadership experience

Role Overview

As a Forward Deployed AI Engineer, you operate on the frontlines of innovation, bridging the gap between cutting-edge AI capabilities and enterprise production systems. You work directly at the intersection of client problem spaces and AI system architecture-designing, building, deploying, and scaling custom AI solutions (including LLM pipelines, RAG architectures, multi-agent workflows, and fine-tuned models) tailored to client environments.

Depending on experience level, you will either lead end-to-end client deliveries and mentor cross-functional teams (Senior/Team Lead) or drive key technical execution and client integration streams under strategic guidance (Intermediate).

Key Responsibilities : Client Engagement & Solution Delivery

• Partner directly with client engineering and business stakeholders to translate complex requirements into robust technical specifications and AI workflows.
• Architect and deploy production-grade AI solutions-including RAG pipelines, fine-tuned models, vector databases, and agent frameworks-within client cloud or on-premises environments.
• Lead integration efforts to connect AI microservices with existing enterprise infrastructure, legacy data stores, APIs, and business systems.
• (Senior/Team Lead) Act as the primary technical authority and trusted advisor to client executive leadership (CTOs, VPs of Engineering) on AI strategy, security, and ROI.

AI Engineering & System Architecture

• Design data orchestration pipelines, vector search infrastructure (e.g., Pinecone, Qdrant, Milvus, PGVector), and framework integrations (e.g., LangChain, LlamaIndex, Semantic Kernel).
• Optimize model latency, throughput, token usage, and computing costs (FinOps) for high-scale enterprise workloads.
• Implement robust evaluation, observability, and safety frameworks to monitor accuracy, drift, hallucinations, and security guardrails in production.
• (Senior/Team Lead) Design distributed systems that meet enterprise compliance and regulatory standards (e.g., HIPAA, GDPR, SOC 2).

Delivery Leadership, MLOps & Quality Assurance

• Establish clean code bases, automated testing, CI/CD pipelines, and enterprise MLOps/LLMOps best practices.
• Troubleshoot performance bottlenecks, system outages, and deployment issues within customer environments.
• Conduct technical enablement and knowledge transfer sessions to empower client internal engineering teams.
• (Senior/Team Lead) Oversee cross-functional delivery teams, mentor intermediate/junior engineers, conduct code reviews, and build reusable engineering playbooks.

Required Qualifications & Skills

• Experience: 3-8+ years in software design, solution engineering, or systems architecture, including proven experience building and deploying production enterprise AI/ML solutions.
• Core Languages: Fluency in Python and modern software engineering practices (TypeScript/Node.js or Go is a plus).
• AI Tech Stack: Expertise with modern AI stacks-PyTorch/TensorFlow, Hugging Face, OpenAI/Anthropic APIs, vector databases, prompt engineering, and agent frameworks.
• Cloud & DevOps: Deep hands-on experience with major cloud providers (AWS, Azure, or GCP), containerization (Docker), CI/CD, and infrastructure concepts (Kubernetes/Terraform preferred for Senior candidates).
• Communication: Outstanding technical communication and stakeholder management skills with the ability to explain complex AI trade-offs to non-technical business leaders.
• Preferred Certifications & Qualifications