S
AI/ML Engineer
Job Description
About The Role
You'll be building the intelligent core of SetuAI — the recommendation systems and adaptive algorithms that make every student's learning path uniquely theirs. This is not a research role; it's applied ML with direct user impact.
You'll work with real data from 50,000+ students to improve our content recommendation engine, difficulty adaptation system, and predictive performance models. Results ship to production, not papers.
What you'll do
Not required — but these will make you stand out.
Fast, transparent, and respectful of your time.
01
2 days
Application Review
02
30 min
Initial Assesment Call
03
2–3 days
Skills Assessment
04
1 hour
Final Interview
Apply for this position
You'll be building the intelligent core of SetuAI — the recommendation systems and adaptive algorithms that make every student's learning path uniquely theirs. This is not a research role; it's applied ML with direct user impact.
You'll work with real data from 50,000+ students to improve our content recommendation engine, difficulty adaptation system, and predictive performance models. Results ship to production, not papers.
What you'll do
- Design and improve the adaptive learning algorithm that personalizes content for each student
- Build and iterate on recommendation models for question selection, topic ordering, and revision scheduling
- Analyse student performance data to find patterns and inform product decisions
- Collaborate with the product team to translate learning science principles into engineering features
- Set up ML pipelines, model evaluation frameworks, and A/B testing infrastructure
- Monitor model performance in production and respond to regressions
- 3+ years of ML/AI engineering experience in a production environment
- Strong Python skills: pandas, scikit-learn, PyTorch or TensorFlow
- Experience with recommendation systems or collaborative filtering
- Comfortable with SQL and working with large structured datasets
- Solid understanding of ML evaluation metrics and A/B testing methodology
Not required — but these will make you stand out.
- Experience in EdTech, knowledge tracing, or cognitive science applications
- Familiarity with LLMs and their application in tutoring or Q&A systems
- Background in Item Response Theory (IRT) or Bayesian Knowledge Tracing
- Competitive salary + ESOPs
- Work on ML problems with real educational impact at scale
- 30,000/yr learning stipend
- Health insurance and wellness benefits
- Hybrid setup in Pune with flexible remote days
Fast, transparent, and respectful of your time.
01
2 days
Application Review
02
30 min
Initial Assesment Call
03
2–3 days
Skills Assessment
04
1 hour
Final Interview
Apply for this position
More Info
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Industry:
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Key Skills
About Company
Skills:
Python, tool function calling, tracing observability tooling, Logfire, AI systems in production, prompting, Temporal, OpenTelemetry, pydantic-ai, LiteLLM, MCP, agent architectures
Skills:
Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Aws Lambda, Python Programming, Devops, MLops, Docker, Kubernetes, LangChain, LangGraph, Generative AI, AI model deployment and monitoring on AWS, Amazon Bedrock, Amazon SageMaker, Prompt Engineering, LLMOps, CI/CD Pipelines, Microservices architecture, Agentic AI, Multi-Agent Systems, API integrations, AI Orchestration
Skills:
Python, Model Lifecycle Management, Model Deployment, ML Pipeline Development, Machine Learning Operations, Cloud AI services, Monitoring Optimization, Machine learning frameworks, Multi Cloud skills
Skills:
Prometheus, Kafka, Grafana, Cloud Storage, Terraform, Postgres, Python, BigQuery, Graphql, Google Cloud Platform, Pulumi, Sql, REST, DataFlow, Kubernetes, Cloud Spanner, Pub Sub, GitHub Actions, Vertex AI, Kubeflow, Cloud Functions, GKE, Cloud Monitoring, Argo CD
Skills:
vertex , bedrock , Tensorflow, Nlp, Pytorch, Gcp, Azure, Python, AWS, MLops, LangChain, CrewAI, LLMs, Generative AI frameworks, Hugging Face, Scikit-learn, MLflow, LangGraph, SageMaker, Vertex AI, Kubeflow, RAG, Polly, Sagemaker, Q Transcribe, Gemini, AutoML