AI/ DevOps Engineer

• The AI / DevOps Engineer is responsible for building, deploying, and operating AI-powered software and the infrastructure that runs it. The role combines hands‑on software development with DevOps and platform engineering, with a strong focus on Large Language Model (LLM) applications, agentic systems, and workflow automation.
• The individual in this role designs and develops AI-enabled applications and automations, integrates LLM APIs and self-hosted models, and builds the pipelines, infrastructure, and observability needed to ship and run these systems reliably across cloud, on‑premise, private cloud, and GPU environments.
• This position bridges development and operations across the full lifecycle, from requirements and system design through CI/CD, deployment, monitoring, security, and post‑production support, while continuously evaluating emerging AI tools and practices to improve efficiency and quality.
• Continuous learning is essential, as the AI / DevOps Engineer must stay current with a fast‑moving AI and infrastructure landscape, including new models, agentic coding tools, orchestration frameworks, and automation techniques.

Responsibilities

AI & LLM Application Development

• Design, develop, and maintain AI-powered applications and services that integrate Large Language Models (LLMs) and other machine learning models into business workflows

Integrate LLM APIs (such as Anthropic Claude and other providers) as well as self-hosted and open-source models running on private GPU infrastructure

• Build retrieval-augmented generation (RAG) pipelines using vector databases, embeddings, and semantic search to ground model outputs in enterprise data
• Design and implement agentic systems and tool-use workflows, including integrations through the Model Context Protocol (MCP) and connections to internal and third‑party services
• Apply prompt engineering, evaluation, and guardrail techniques to improve accuracy, safety, reliability, and cost‑efficiency of AI features
• Write clean, efficient, reusable, and well‑tested code following established standards and secure coding practices

.Software Development & Integration

• Design and develop scalable, secure backend services, APIs, and integrations that support AI and automation use cases
• Perform application and data integration with internal and external systems using RESTful APIs, web services, webhooks, and message queues
• Translate business requirements into functional and technical specifications, and participate in architecture and design discussions
• Ensure solutions are compatible across multiple platforms and environments, including cloud, on‑premise, and private cloud deployments

.Workflow & Process Automation

• Design, develop, and deploy automation workflows that combine traditional automation with AI-driven decision‑making
• Build automations using modern tools and platforms such as Power Automate, n8n, Zapier-style iPaaS, and custom scripts, replacing manual and repetitive processes
• Develop and operate desktop and agentic automation, including AI desktop agents (for example, agent-based assistants such as Cowork / Open Claw-style tools) that perform tasks across applications
• Implement web automation and data extraction where required using tools such as Playwright, Puppeteer, or Selenium
• Use agentic coding tools such as Claude Code to accelerate development, automate engineering tasks, and build internal tooling
• Automate IT and operational workflows such as provisioning, monitoring, alerting, ticketing, and incident response
• Build, maintain, and optimize infrastructure across cloud (AWS, GCP), on‑premise, and private cloud environments for efficiency, scalability, and reliability
• Provision and manage GPU compute for model inference and AI workloads, optimizing for performance and cost
• Design and maintain CI/CD pipelines to automate building, testing, and deployment of applications, models, and automations
• Manage source control and Git‑based workflows on platforms such as GitHub, GitLab, or Bitbucket, including branching strategies, pull/merge requests, and code review processes
• Containerize and orchestrate workloads using Docker and Kubernetes, and manage infrastructure as code (e.g., Terraform)
• Manage deployment, release, and configuration management, and support smooth promotion of changes from development to production
• Administer Linux/Unix and Windows environments supporting development and production systems

Monitoring, Reliability & Performance

• Implement monitoring, logging, alerting, and observability for applications, infrastructure, and AI/LLM workloads using tools such as Prometheus, Grafana, the ELK/Loki stack, Datadog, or cloud‑native services (e.g., AWS CloudWatch)
• Track AI‑specific metrics such as latency, token usage, cost, accuracy, and quality, and act on the results
• Proactively identify, troubleshoot, and resolve performance issues and production incidents within agreed timelines
• Participate in root cause analysis and drive preventive improvements to system reliability and stability

.Security & Compliance

• Apply security best practices across development, automation, and operations, including secrets management, access control, and network security
• Address AI-specific security and governance concerns such as data privacy, prompt injection, safe handling of sensitive data, and responsible use of models
• Ensure activities comply with organizational policies, security standards, and audit requirements, and maintain proper version control and documentation
• Work closely with developers, data and AI engineers, operations staff, business analysts, and other stakeholders to deliver end-to-end solutions
• Participate in Agile/Scrum ceremonies including sprint planning, daily stand‑ups, reviews, and retrospectives
• Act as a liaison between technical teams and stakeholders, and communicate solutions, trade‑offs, and results clearly
• Prepare and maintain technical documentation, including system designs, runbooks, and operational procedures
• Continuously evaluate and recommend new models, tools, frameworks, and practices to improve productivity and quality.

Qualifications:

• Bachelor’s degree in Computer Science, Information Technology, Software Engineering, or a related field (or equivalent practical experience
• At least 2–4 years of combined experience across software development, DevOps, or automation; experience with AI/LLM-based solutions is strongly preferred
• Demonstrated experience building and deploying applications in cloud, on‑premise, or private cloud environments
• Experience integrating APIs and third‑party services, and building automated workflows
• Working knowledge of Agile/Scrum methodologies and collaboration tools
• Relevant certifications in cloud (AWS, GCP), DevOps, or AI/ML are an advantage

Technical Skills

• Programming Languages & Core Stackack Python (required, primary): main language for AI/LLM development, automation, data work, and scripting; experience with frameworks and libraries such as FastAPI or Flask, plus LangChain, LlamaIndex, or the Anthropic and OpenAI SD
• PowerShell (preferred): for Windows administration and automation in mixed environments
• Go and/or C# (advantageous): for performant backend services, infrastructure tooling (Go), or .NET-based enterprise integrations (C#).
• Configuration and infrastructure-as-code languages: YAML and JSON for pipelines and config, and HCL (Terraform) for provisioning infrastructure
• Strong proficiency in the core languages above, with the ability to pick up additional languages as project needs evolve
• Hands‑on experience integrating LLM APIs (e.g., Anthropic Claude, OpenAI) and building AI features such as RAG, agents, and tool use.
• Familiarity with AI frameworks and libraries such as LangChain, LlamaIndex, or similar, and with vector databases (e.g., Pinecone, Weaviate, pgvector, or FAISS)
• Experience with the Model Context Protocol (MCP) and agentic coding tools such as Claude Code is an advantage
• Strong knowledge of cloud platforms, especially AWS and GCP, plus on‑premise and private cloud deployment
• Experience provisioning and using GPU compute for model inference and training
• Solid DevOps skills: CI/CD pipelines (e.g., GitHub Actions, GitLab CI/CD, Jenkins), Docker, Kubernetes, and infrastructure as code (e.g., Terraform)
• Experience with Linux/Unix and Windows administration and scripting (e.g., Bash, Python Knowledge of RESTful APIs, JSON, webhooks, and system integration concepts
• Experience with databases including PostgreSQL, MySQL, MongoDB, or SQL Server
• Familiarity with workflow automation tools (e.g., Power Automate, n8n) and web automation (e.g., Playwright, Puppeteer, Selenium)
• .Strong experience with Git and Git‑based platforms such as GitHub, GitLab, and Bitbucket, including branching strategies, pull/merge requests, code review, and repository management, along with modern software development life cycle practices.
• Hands‑on experience with monitoring and observability tooling such as Prometheus, Grafana, the ELK/Loki stack, Datadog, or cloud‑native services (e.g., AWS CloudWatch), including metrics, logs, dashboards, and alerting for applications, infrastructure, and AI workloads.
• Awareness of AI safety, security, and governance considerations, including data privacy and prompt‑injection risks.