AI/DevOps Engineer

Job Description

Job Overview · 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. Infrastructure, Cloud & DevOps · 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. Collaboration & Continuous Improvement · 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 Stack · 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 SDKs. · TypeScript / JavaScript (required): for backend services (Node.js) and front-end or full-stack work (React or similar), API integrations, and building agentic and MCP-based tooling. · Bash / Shell scripting (required): for automation, CI/CD, and Linux/Unix system administration. · SQL (required): for querying and managing relational databases such as PostgreSQL, MySQL, or SQL Server. · 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.