AI Engineer

AI Engineer

Experience: 3 – 6 years

Engagement: Full-time

Core stack: Python · AWS · LLMs

Education: BSc / MSc preferred

About the role

We are a technology company building an innovative AI-powered product that pushes the

boundaries of how people interact with intelligent systems. You will be a key contributor

from the early stages, shaping both the product's technical architecture and its direction. If

you are passionate about applied AI and want your work to have a genuine impact, this is

the role for you.

What you'll do

Generative AI development. Fine-tune and prompt-engineer LLMs for specific use

cases, focusing on language understanding, tone, and contextual reasoning.

AWS cloud architecture. Design and scale the backend using AWS services

(Bedrock, SageMaker, Lambda, S3) to support production-grade AI workloads.

Data orchestration & ETL. Build robust pipelines to ingest and process structured

and unstructured data from diverse sources, transforming it into high-quality

knowledge bases.

Agentic & memory systems. Implement RAG pipelines and vector databases so AI

systems can retrieve context and reason across extended interactions.

Ethical AI & privacy. Design privacy-first protocols and safety guardrails to handle

sensitive data responsibly and in compliance with applicable regulations.

Technical stack

Core: Python, SQL, FastAPI

Cloud: AWS Bedrock, SageMaker, Lambda

AI / ML: LLMs, RAG, Embeddings

Data: ETL pipelines, PostgreSQL, NoSQL

Vector databases and embeddings: Pinecone, Weaviate, pgvector

Collaboration: Git / Gitflow, Docker, Agile

Required qualifications

Education: Bachelor's or Master's in Statistics, Computer Science, Data Science,

AI, or a related quantitative field.

Experience: 3–6 years of hands-on work in Data Science or AI Engineering with

models deployed to production.

Python expertise: Advanced proficiency. Model training, API development, ETL

scripting, and optimization.

Generative AI: Demonstrated experience with LLMs, prompt engineering, RAG

architectures, and vector databases.

AWS: Hands-on production experience with core AWS services for ML workloads

(SageMaker, Lambda, S3, Bedrock).

Data engineering: Ability to build end-to-end pipelines for structured and

unstructured data from diverse sources.

Nice to have

AWS Certified AI Practitioner or AWS Machine Learning Specialty certification.

MLOps experience: CI/CD for ML, experiment tracking, and automated model

monitoring.

Experience with NLP, speech-to-text, or multimodal data (voice, image).

Familiarity with agentic frameworks (LangChain, LangGraph, AutoGen).

Background in computer vision or deep learning.

Knowledge of data privacy regulations and responsible AI frameworks.

Experience in a startup or product-building environment.