We are seeking an MLOps Engineer who is a backend-leaning software engineer at heart, and who can also take models from experimentation all the way into reliable production. You do not need to be a research scientist for this one. The emphasis here is on engineering models into services, deploying them well, and keeping them running smoothly once they are live.
At Impact Analytics, machine learning is not a lab experiment on the side; it is the product. The services you build will turn our data and ML teams’ work into real, dependable capabilities that retailers across five continents rely on every day. When a model you deployed serves predictions cleanly at scale, that reliability is felt far beyond our codebase.
What we value most is a strong foundation in Python and a genuine enthusiasm for continued learning, the kind of individual inclined to independently develop a proof of concept. A working (not necessarily deep) understanding of ML models and the MLOps lifecycle is what sets the right person apart for this role, and if you are strong on the engineering side and eager to grow the ML-operations side, we would love to hear from you.
What this role entails
Build and maintain FastAPI services that expose ML model functionality — clean, well-designed APIs that make models easy and safe for the rest of the platform to consume, with full ownership of the code you produce.
Deploy, version, and monitor models in production on Google Cloud Platform — stand up inference endpoints, manage model versions, and keep an eye on how they behave once they are serving real traffic. Prior experience with our specific tools is not required; you will receive the support and mentorship needed to develop proficiency.
Operationalize models reliably and cost-effectively — collaborate closely with data and ML stakeholders to take trained models from notebook to a service that is dependable, observable, and mindful of the compute it consumes.
Contribute to the shared backend codebase and engineering practices — constructive code reviews, thorough testing, and clear documentation that keeps the whole team moving.
Take initiative in exploring new tools, technologies, and approaches — team members are encouraged to prototype and propose improvements, with the strongest ideas adopted regardless of their source. If a new serving pattern or orchestration tool could make things better, prototype it and show us.
What lands you in this role
1–3 years of strong Python development, including writing and consuming APIs with FastAPI (experience with Flask or Django and a willingness to adopt FastAPI is also welcome).
Practical MLOps or ML model knowledge — hands-on experience serving and deploying trained models, building inference endpoints, and managing model versions.
A conceptual understanding of the ML lifecycle — data preparation, the difference between training and inference, evaluation metrics, and model monitoring and drift. You do not need to build the models, but you should understand how they live and behave.
Hands-on GCP experience, ideally with ML-relevant services (e.g. Vertex AI, Cloud Run, GCS, Pub/Sub), or a genuine willingness to ramp quickly. Coming from equivalent AWS or Azure experience is fine too, as the underlying concepts transfer.
Containerization with Docker and comfort working within CI/CD pipelines.
Sound software engineering fundamentals — version control (Git), automated testing, code review, and a real commitment to clean, maintainable code over code that merely works today.
Good to Have
Familiarity with ML frameworks (e.g. scikit-learn, PyTorch, or TensorFlow) and feature/experiment tracking tools such as MLflow.
Experience with workflow and pipeline orchestration (e.g. Airflow, Kubeflow, or Vertex Pipelines).
Exposure to data stores and processing — BigQuery, SQL/NoSQL, and both batch and streaming workloads.
Infrastructure-as-code (e.g. Terraform) and Kubernetes (GKE).
What we offer
An opportunity to be part of some of the best enterprise SaaS products to be built out of India.
Opportunities to quench your thirst for problem-solving, experimenting, learning, and implementing innovative solutions.
A flat, collegial work environment, with a work hard, play hard attitude.
A platform for rapid growth if you are willing to try new things without fear of failure.
Remuneration with best-in-class industry standards with generous health insurance cover