AI Engineer
CodeRound is hiring for this role for a VC-backed startup.
- AI Engineer
- Machine Learning
- Artificial Intelligence
- Generative AI
- LLM
- RAG
- Agentic AI
- Python
- PyTorch
- TensorFlow
- Scikit-learn
- XGBoost
- NLP
- Vector Database
- Embeddings
- Semantic Search
- MLOps
- AWS
- GCP
- Azure
- Docker
- SQL
- Credit Risk
- Fraud Detection
- Fintech
We are looking for an AI Engineer to build production-grade AI/ML solutions across credit risk, fraud detection, customer analytics, and GenAI use cases. You will work across LLM applications, RAG, agentic AI, machine learning, and MLOps, turning models and AI capabilities into scalable products in a high-impact fintech environment.
What you'll do
- Build and deploy LLM-powered applications, including RAG pipelines and AI agents.
- Develop and evaluate AI solutions for credit risk, fraud, anomaly detection, and customer segmentation.
- Perform feature engineering, model selection, tuning, validation, and error analysis.
- Integrate vector databases, embeddings, and semantic search into AI applications.
- Experiment with and deploy open-source/self-hosted LLMs for production use cases.
- Build scalable ML pipelines and data workflows across cloud infrastructure.
- Work closely with data engineers and product teams to take models from experimentation to production.
- Develop reusable AI/ML tooling and libraries with strong engineering practices.
- Monitor model performance and continuously improve accuracy, reliability, latency, and scalability.
- Translate business and financial problems into practical AI/ML solutions.
Must have
2–4 years of hands-on experience in AI/ML engineering, including strong project or production exposure. Strong Python programming skills with clean, maintainable, production-ready code. Hands-on experience building LLM applications, prompt engineering, and RAG pipelines. Understanding of vector embeddings, semantic search, vector databases, and knowledge retrieval. Familiarity with Hugging Face Transformers, OpenAI APIs, or equivalent LLM frameworks. Experience with scikit-learn and either PyTorch or TensorFlow, along with XGBoost, pandas, and NumPy. Strong SQL skills for querying, transforming, and analysing structured data. Experience with ML workflows including feature engineering, model training, evaluation, tuning, and error analysis. Working knowledge of at least one cloud platform: AWS, GCP, or Azure. Understanding of Docker/containerization and production ML deployment.
Good to have
Experience building agentic AI systems using frameworks such as LangGraph or similar. Experience with self-hosted LLM inference using vLLM, Ollama, or similar technologies. Knowledge of MLOps, MLflow, Weights & Biases, model monitoring, and experiment tracking. Experience with MCP or AI tool integrations. Exposure to Spark/Hadoop or large-scale data processing. Strong understanding of ensemble methods, cross-validation, hyperparameter optimisation, and anomaly detection. Experience with A/B testing, multivariate experiments, and statistical analysis. Fintech experience across credit scoring, lending, fraud detection, risk analytics, or financial inclusion. Strong GitHub/open-source portfolio or meaningful AI/ML projects.