AI Engineer

Job Title: AI Engineer

Employment Type: Contract (3 Months, Extendable)

Work Arrangement: Fully Remote

Experience: Minimum 3 Years

About the Role

We are looking for an experienced AI Engineer with a strong product mindset and hands-on engineering expertise to take AI products from concept to production.

In this role, you will own the end-to-end product development lifecycle, from understanding business needs and defining technical requirements to building full-stack applications, developing AI-powered systems, deploying models, and maintaining production infrastructure.

You will also leverage AI coding agents to accelerate engineering workflows, translate requirements into actionable tasks, and ensure reliable, scalable, and cost-effective solutions.

Key Responsibilities

1. Product Development & AI Task Planning

• Collaborate with stakeholders to understand user needs, define requirements, and prioritize features based on business impact.
• Translate product requirements into clear, well-scoped engineering tasks with acceptance criteria for AI coding agents.
• Review, validate, and refine AI-generated code to ensure quality and reliability.
• Deliver features incrementally, monitor usage, and continuously improve based on user feedback.

2. Full-Stack & Data Engineering

• Develop and maintain front-end applications using React, Next.js, or similar frameworks.
• Build back-end services and APIs using Python/FastAPI, Node.js, or equivalent technologies.
• Design database schemas and manage relational, NoSQL, and vector databases.
• Develop and maintain production data pipelines for data ingestion, transformation, and scheduling using tools such as Airflow, dbt, Kafka, or similar.

3. AI Deployment & Model Serving

• Manage end-to-end deployments across applications, databases, data pipelines, and AI models.
• Build and maintain CI/CD pipelines, including safe deployment, rollout, and rollback processes.
• Deploy open-weight and fine-tuned models using inference servers such as vLLM, TensorRT-LLM, SGLang, or Triton.
• Optimize model performance through quantization, batching, and caching to meet latency, throughput, and cost targets.
• Containerize and orchestrate services using Docker and Kubernetes, including autoscaling.
• Establish monitoring, logging, alerting, and reliability practices for applications and model endpoints.

4. Infrastructure & Cost Management

• Set up and maintain GPU infrastructure for model serving across cloud platforms or GPU providers.
• Monitor and optimize cloud infrastructure and compute costs.

5. Agentic Systems & AI Automation

• Build and operate AI agents using frameworks and tools such as Claude Code, Claude Agent SDK, LangGraph, or similar.
• Design tools, MCP servers, prompts, and context management strategies to support reliable multi-step agent workflows.
• Develop evaluation frameworks to measure agent success rates, costs, and failure modes.
• Integrate AI coding agents into daily engineering workflows and support broader team adoption.

6. Collaboration & Documentation

• Work closely with product and engineering teams to deliver production-ready AI features.
• Prepare clear technical design documents, runbooks, and incident reports in English.
• Communicate technical decisions and trade-offs effectively with technical and non-technical stakeholders.

Requirements

• Minimum 3 years of professional experience in Software Engineering, AI Engineering, or Machine Learning Engineering.
• Proven experience owning features end-to-end, from requirements gathering and development to production launch and post-release improvement.
• Strong product mindset with the ability to assess business value, prioritize tasks, and propose practical solutions.
• Experience translating requirements into structured engineering tasks for AI coding agents.
• Full-stack development experience across front-end, back-end, and databases, with hands-on experience in modern frameworks and technologies.
• Experience building and operating production data pipelines.
• Strong deployment and infrastructure skills, including CI/CD, Docker, Kubernetes, and at least one major cloud provider.
• Demonstrated experience deploying and serving AI/ML models in production independently, beyond model training.
• Working knowledge of GPU environments, including drivers, CUDA, and container runtimes.
• Practical experience building agentic systems using agentic harnesses or frameworks, including tool use, multi-step workflows, and agent evaluation.
• Strong Python programming skills and solid Linux, shell scripting, and networking fundamentals.
• Excellent written and spoken English communication skills.

Nice to Have

• Experience fine-tuning LLMs using LoRA, QLoRA, or full fine-tuning techniques and deploying the resulting models.
• Experience with multi-node distributed inference or training.
• Infrastructure as Code experience using Terraform or similar tools.
• Experience developing MCP servers and custom AI agent tool integrations.
• Contributions to open-source AI or infrastructure projects.

What We Are Looking For

• A hands-on engineer who combines product thinking, full-stack development, AI infrastructure expertise, and agentic engineering capabilities, with the ability to independently deliver reliable AI-powered products from idea to production.