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.

Work arrangement
Yes