Junior AI/ML Engineer at TensorOps

Key Facts

Remote From:

Full time

Junior (1-2 years)

English

Hard Skills

Python (Programming Language) Machine Learning Software Engineering FastAPI Kubernetes LightGBM PyTorch (Machine Learning Library) MLflow Elasticsearch gRPC +2 more

Other Skills

•
Communication

Roles & Responsibilities

BSc in Computer Science, Software Engineering or equivalent
MSc in Computer Science, Data Science, AI or equivalent
Solid software engineering fundamentals (OOP, Git, concurrency, parallelism)
Proficiency in Python

Requirements:

Help deliver projects rapidly
Work on real projects that make a tangible impact
Report to and be mentored by a senior team member

TensorOps

About TensorOps

TensorOps is a leading provider of machine learning (ML) and artificial intelligence (AI) services for startups and companies. The company specializes in two key areas: Search Relevance and TimeSeries. Search Relevance: TensorOps helps companies improve their search algorithms to deliver more relevant results to their customers. This involves analyzing search queries, optimizing ranking algorithms, and developing custom solutions to improve search relevance. By improving search relevance, companies can increase customer engagement, retention, and ultimately revenue. TimeSeries: TensorOps provides expertise in TimeSeries forecasting, which is a technique used to predict future values based on historical data. This is particularly useful for companies that deal with time-sensitive data, such as financial data, healthcare data, and manufacturing data. TensorOps works with companies to develop custom forecasting models that can help them make more accurate predictions, improve planning, and optimize resource allocation. TensorOps is known for its high level of expertise, customer service, and attention to detail. The company works closely with clients to understand their needs and develop tailored solutions that deliver tangible business results. Whether it's improving search relevance, optimizing forecasting models, or developing custom ML and AI solutions, TensorOps is committed to helping its clients succeed in today's fast-paced business environment.

Founded: 2018

Company size: 2 - 10

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Job description

Build the Next Generation of AI Products with TensorOps

TensorOps is an applied machine learning and artificial intelligence studio helping organizations worldwide plan, design, train, and deploy production-grade ML systems. Our clients range from NASDAQ-listed enterprises to seed-stage startups. Projects span from small proofs-of-concept to multi-year strategic initiatives.

What We’re Working On:

Generative AI applications: Chatbots and Agents
Traditional Machine Learning: Time Series Forecasting, AdTech, Computer Vision, etc.
MLOps: Improving ML pipelines at scale

Core Stack:
As we work with many clients, our stack varies, but we often use:

Python APIs: FastAPI
Containerization: Docker, Kubernetes
Model Training & Serving: LightGBM, CatBoost, PyTorch, HuggingFace
Data Engineering: Pandas, Polars
LLM Frameworks: LangChain, LangGraph
Observability: MLFlow, Langfuse
Cloud Platforms: AWS, GCP
Search: Elasticsearch, OpenSearch, Solr

The Role:
We’re looking for a Junior Machine Learning Engineer to help us deliver projects rapidly. You’ll report to and be mentored by a senior team member. This is a hands-on role from day one, working on real projects that make a tangible impact.

Preferred Qualifications:

BSc in Computer Science, Software Engineering or equivalent
MSc in Computer Science, Data Science, AI or equivalent

Required Skills:

Solid software engineering fundamentals (OOP, Git, concurrency, parallelism)
Proficiency in Python
Understanding of LLM system design (RAG, agents, etc.)
Knowledge of ML system design (pipelines, training/inference techniques)
Excellent English communication skills

Nice to Have:

Experience in non-academic projects (jobs, internships or similar)
Previous LLM projects (academic or otherwise)
Exposure to AI features in cloud platforms (Sagemaker, Bedrock, Vertex AI)
Experience working in large codebases

Why TensorOps?

Fully remote (legal residence in Portugal required)
Real-world projects, rapid feedback loops, and measurable impact
Mentorship from engineers who have shipped ML systems at scale
Competitive compensation and growth opportunities - your growth will be based on ownership and performance rather than periodic reviews (which we still do)

Compensation & Perks: