AI DevOps Engineer
7y relevant experience
EU engineers, ready to place with your US clients
Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.
Executive Summary
K H Vishwanathan is a technically strong AI DevOps/MLOps engineer whose skill set aligns exceptionally well with the core requirements of this role, particularly in ML pipeline deployment, observability, IaC, and Kubernetes orchestration. their 10+ years of experience — including recent hands-on work with RAG-based LLM deployments and enterprise-grade MLOps tooling — places him well above the minimum 4-year threshold. The primary areas to validate before extending an offer are the depth of their GCP/AWS production experience (vs. Azure dominance) and their appetite for a stable full-time role given a recent pattern of short-term contracts. With no major red flags and a strong technical profile matching 85%+ of required skills, they is recommended for a technical interview.
Top Strengths
- ✓Production MLOps expertise with Kubeflow, MLflow, and LLM/RAG deployments — rare and directly relevant
- ✓Full-stack observability mastery across Prometheus, Grafana, ELK, DataDog, OpenTelemetry, and Cribl
- ✓Strong IaC proficiency with Terraform and Ansible across multi-cloud environments
- ✓Dual MS credentials in Data Science providing strong theoretical ML foundation alongside DevOps practice
- ✓Team lead and cross-functional collaboration experience aligning well with the small platform team structure
Key Concerns
- !Series of short contractual engagements (3-6 months) raises questions about commitment and potential flight risk in a growth-stage startup
- !Azure-dominant experience with limited demonstrated depth in GCP/AWS production, which are explicitly required
Culture Fit
Growth Potential
High
Salary Estimate
$90k-$110k (upper mid-range given 10+ years experience and dual MS degrees, though contract background may shift expectations)
Assessment Reasoning
This candidate is based on the candidate meeting or exceeding requirements across all eight required skills: Kubernetes (AKS + OpenShift production), CI/CD Pipelines (GitHub Actions, Azure DevOps, Jenkins), Docker, MLOps (Kubeflow, MLflow, RAG/LLM deployments), AWS/GCP (multi-cloud exposure), Infrastructure as Code (Terraform, Ansible, Bicep), Python, and Terraform. their 10+ years of experience significantly exceeds the 4-year minimum, and preferred qualifications around MLOps tools, observability stacks, and AI/ML company background are all satisfied. This candidate is held to 82 rather than higher due to: (1) inability to verify LinkedIn data or GitHub code quality, (2) Azure-heavy experience requiring AWS/GCP depth validation, and (3) short-tenure contract pattern warranting discussion. The candidate clears the 70+ threshold comfortably and is recommended to advance to technical interview.
Interview Focus Areas
Code Review
No GitHub profile or code samples were provided, making direct code quality assessment impossible. Based on resume descriptions and academic project details, the candidate demonstrates practical scripting and ML coding ability consistent with a Senior-level engineer. A technical interview or take-home exercise is strongly recommended to validate hands-on coding depth.
- +Resume demonstrates scripting proficiency across Python, Bash, and PowerShell in automation contexts
- +Academic projects show structured use of NLP libraries, ML frameworks, and evaluation methodologies
- +Evidence of production-grade pipeline development suggests solid engineering discipline
- -No GitHub profile provided — cannot directly assess code quality or style
- -No open-source contributions mentioned despite preferred qualification asking for them
- -Code samples unavailable to verify depth of Python automation skills claimed
Experience Overview
11y total · 7y relevantK H Vishwanathan presents an exceptionally well-matched profile for an AI DevOps Engineer role, with 10+ years of experience that directly spans MLOps, DevOps, and cloud infrastructure. their recent roles at EPAM/ServiceNow and Kion Group AG demonstrate hands-on production experience with exactly the tooling stack required. The candidate's dual MS degrees in Data Science combined with extensive observability and ML pipeline expertise make him a strong technical fit, though their Azure-heaviness and pattern of short-term contracts warrant further discussion.
Matching Skills
Skills to Verify