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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level AI engineering attracts applicants but niche LLM/MLOps skills limit broad competition.
Requires specialized enterprise LLM, vector DB, and MLOps expertise, limiting cross-industry transferability.
Multiple mandatory AI, MLOps, cloud, and leadership requirements make shortlisting stringent.
Job Description
Structured overview of role & requirementsAbout This Role
Design, build, deploy, and scale custom AI solutions (LLM pipelines, RAG architectures, multi-agent workflows, fine-tuned models) tailored for enterprise client environments.
Lead end-to-end client AI solution delivery, including architecting production-grade AI systems and integrating with existing enterprise infrastructure.
Oversee team delivery, mentor engineers, establish MLOps best practices, and provide technical advisory to client executives on AI strategy and compliance (Senior/Team Lead).
Minimum Requirements
3–8+ years experience in software and systems engineering with proven enterprise AI/ML production delivery.
Proficient in Python and modern software engineering practices; experience with AI frameworks (PyTorch/TensorFlow, Hugging Face, OpenAI APIs).
Hands-on experience with major cloud platforms (AWS/Azure/GCP), containerization (Docker), CI/CD; Kubernetes/Terraform experience preferred for senior roles.
Work Experience Required: 3–8+ years in relevant fields.
Ideal Candidate Profile
Experienced in directly engaging with clients to translate complex AI requirements into deployable enterprise solutions.
Skilled at designing distributed, compliant AI architectures with operational focus on system performance, security, and cost efficiency.
Capable of leading cross-functional teams and enabling client engineering orgs through mentorship, technical leadership, and knowledge transfer.