AI Engineer

RebelDot future-proofs its services line by acquiring the market leader in AI capabilities, Steepsoft.

About the role:

Steepsoft, a leading software development company specializing in Artificial
Intelligence, Computer Vision, Data Engineering, and Web/Mobile development, is
looking to add an AI Engineer to our cross-functional team.

As an AI Engineer you will be responsible for developing and maintaining data pipelines, implementing or integrating APIs and working with a web development team to deliver these services to our users.

Also, AI Engineering at steepsoft requires more than just research and modeling skills. It demands strong software engineering abilities, including clean coding, proficiency in AI engineering frameworks, and experience with server-side frameworks such as FastAPI for building high-performance APIs in Python. Knowledge of best practices like version control, TDD and software development lifecycle is essential.

You will work with a team of skilled members, on tasks that range from data science, image generation, image processing, to LLMs, NLP, and transformer-based architectures.

What we are looking for:

• A proactive, humble individual with an insatiable desire to learn and a passion for building a high-performing team that can deliver on ambitious goals
• Passion for technology. You love coding, endless conversations on dev tools, frameworks, efficiency, and geek out over intricacies of software
• Research mindset. We’re science-focused and product-driven. At steepsoft, we turn AI into practical products. We’re looking for someone that owns an AI feature end to end, which includes (but not limited to) building server-side software that delivers AI inference results, aiming for reliable, scalable systems that seamlessly integrate into client products. This means that your skill stack will consist even of high-level frameworks such as LangChain/Haystack for LLM interaction, or 3rd party vendor SDKs such as OpenAI’s SDK
• Collaboration. You’ll work with AI Product Managers to translate client needs into technical specs and collaborate with engineers to solve challenges. Your ability to communicate complex AI concepts to both technical and non-technical stakeholders is crucial for the success of our projects. Can you explain how a CNN works to a non-technical person? You’re the one!

You might be our missing piece if you have:

• Strong expertise in Python and Python-based AI frameworks such as PyTorch (ideal), Keras, SciPy, or Tensorflow
• Expertise in Python-based Web frameworks such as FastAPI, Flask or Django (if you have worked with at least one, it’s great)
• Proficiency in Docker (most of our products run on Docker, therefore having the basic knowledge of running and pushing containers would be a great advantage that you can bring to the team). Of course, complex pipelines will be implemented by DevOps teams, but we strongly believe that an engineer should not only build, but also understand how to serve results.
• Familiarity with databases for storing and retrieving data for AI models is essential. Proficiency with SQLAlchemy, a SQL toolkit and Object-Relational Mapping (ORM) library for Python, as well as Alembic, a lightweight database migration tool used with SQLAlchemy, is also required. Any system reads, transforms, stores, and serves data. This is not an exception
• Experience managing datasets using tools such as Pandas, SciPy, Numpy, or any other tools to get data moving (data requires a lot of pre/post-processing and we will rely on your engineering skills to make sure that our users’ data is properly processed, stored and used)
• Solid experience in managing integrations and APIs (we are using multiple APIs that help us solve NLP or other AI problems; integrations such as: OpenAI, Anthropic, and more)
• Working experience with databases and data serializers
• PEP 8

We would be thrilled if you have:

• Extensive experience in addressing AI/ML challenges
• Research experience – have you implemented scientific papers before?
• Experience with OpenAI and other LLM providers (Embeddings, Completion, Semantic Search, Image handling, Audio handling)
• Experience with any cloud provider (Azure, AWS, DigitalOcean or GCP)
• Experience with StableDiffusion, Llama, and other hardware-hungry AI repositories