Engineer Data

Company details

Description of the offer

 Responsibilities :
Lead the AI engineering roadmap — Own the design, development, and deployment of AI systems, especially those leveraging large language models (LLMs) and generative AI frameworks.

Build production-grade pipelines — Architect and deploy end-to-end solutions using tools like Python, PostgreSQL, Redis, S3, and Vector DBs.

Fine-tune and optimize LLMs — Customize foundation models to meet specific business objectives, and contribute to model selection, fine-tuning, prompt engineering, and performance evaluation.

Ensure scale, cost, and performance — Identify and eliminate bottlenecks across infrastructure, compute, and data. Monitor system health and optimize for cost and latency on AWS and GCP.

Collaborate with a cross-functional team — Work closely with product leads, data scientists, backend engineers, and DevOps to ship quickly and reliably.

Build and mentor the AI team — Help grow a team of engineers and guide them through architectural decisions, coding best practices, and delivery.

Stay ahead of the curve — Track emerging trends in AI research, open-source tools, and developer ecosystems, and apply them pragmatically to our use cases.

Automate and own the infra — Champion CI/CD best practices, infrastructure-as-code, and reproducible research pipelines.

 Tech Stack You’ll Work With
Languages & Frameworks: Python (FastAPI, LangChain, etc.)

Cloud & Infra: AWS (EC2, S3, Lambda), GCP, CI/CD pipelines

Databases: PostgreSQL, Redis, DynamoDB, Vector DBs (e.g., Pinecone, Weaviate, pgvector)

AI/ML Tools: OpenAI APIs, Hugging Face, fine-tuning libraries, embedding generators

Orchestration & Ops: Docker, GitHub Actions, Terraform (or similar), Prefect/Airflow

✅ What We’re Looking For

10+ years of hands-on engineering experience, with a strong focus on AI/ML systems

Proven experience working with LLMs, generative AI applications, or AI agents in production

Expertise in building scalable backend/data systems with PostgreSQL, Redis, and S3

Comfort with cloud-native development (AWS/GCP), cost monitoring, and performance tuning

Solid understanding of software architecture, CI/CD, and containerized deployments

Ability to translate business needs into technical strategies and lead delivery end-to-end

Strong communication and leadership skills — able to lead discussions, mentor teammates, and deliver in fast-paced environments

** Nice to Have**
Experience in LLM fine-tuning, RAG pipelines, or retrieval-augmented systems

Familiarity with AI research or staying plugged into the latest from arXiv

Background in CLI tooling, automation scripts, or rapid prototyping

Prior experience scaling ML systems under resource constraints

Contributions to open-source AI tools or ML frameworks

Location of employment

How to apply?