Senior Technical Consultant - AI Platform Engineer
New
J
JobgetherIT Consulting
IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 8+ years of professional IT experience, including at least 4 years in cloud, platform engineering, DevOps, automation, application modernization, or related fields.
- Required Skills
- PythonCloud ComputingKubernetesCI/CDDevOpsTerraformLLMGenerative AI
Requirements
- Bachelor’s degree in Computer Science, Engineering, IT, or equivalent practical experience.
- 8+ years of professional IT experience, including at least 4 years in cloud, platform engineering, DevOps, or modernization.
- Hands-on experience with at least one major cloud platform: AWS, Azure, or Google Cloud.
- Proven experience delivering cloud migration initiatives including landing zones and dependency analysis.
- Strong proficiency in Infrastructure as Code technologies such as Terraform or Bicep.
- Practical expertise with CI/CD, containerization, Kubernetes, and cloud-native architectures.
- Strong scripting or programming capabilities using Python, PowerShell, or Bash.
- Experience using AI coding assistants and agentic development tools with rigorous human review.
- Knowledge of LLMs, RAG, prompt engineering, AI agents, and model APIs.
- Strong consulting, technical writing, presentation, and stakeholder-facing skills.
- Ability to translate complex technical requirements into practical business solutions.
Responsibilities
- Lead customer workshops, technical discovery sessions, architecture discussions, and solution design activities.
- Design and implement scalable platforms supporting cloud-native applications, data platforms, and AI-enabled workloads.
- Lead cloud migration and modernization initiatives from assessment through operational handoff.
- Build reusable platform patterns, Infrastructure as Code, automation, and self-service developer tooling.
- Lead AI-accelerated development initiatives by integrating AI tools and agentic practices into engineering workflows.
- Support organization-wide AI enablement through assessments, pilots, governance frameworks, and adoption roadmaps.
- Design and integrate AI application patterns involving LLMs, RAG, agents, vector databases, and model APIs.
- Mentor engineers and contribute to internal technical communities and knowledge-sharing initiatives.
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