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7 to 15 or more years of experience in software engineering, data science, or ML engineering
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Strong background in product companies, scale-ups, or enterprise AI platforms
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Proven track record of building production-grade AI systems, not solely notebooks or proof-of-concept work
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Comfortable owning systems end-to-end from data through model through deployment through monitoring
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Product-first engineering approach, not research-only profiles
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Advanced Python engineering skills with strong systems thinking and a focus on production quality
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Comfortable with fast iteration cycles and deploying models into live environments
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Ability to work directly and confidently with stakeholders and product owners
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Fintech or financial services experience is an advantage
Machine Learning and AI: PyTorch, TensorFlow, XGBoost, LightGBM, Hugging Face (Transformers, Datasets, Diffusers)
LLM and GenAI: OpenAI and Anthropic APIs, LangChain, LlamaIndex; RAG architectures with vector DB and retrieval pipelines; embedding models (OpenAI, Cohere, open-source); Pinecone, Weaviate, Milvus, FAISS; fine-tuning via LoRA and PEFT frameworks; evaluation using RAGAS and custom pipelines
MLOps and Production: Docker, Kubernetes, MLflow, Weights and Biases, Airflow, Dagster, Prefect, GitHub Actions, GitLab CI, Evidently AI, Arize, custom observability stacks
Cloud: AWS (SageMaker, EKS, S3, Lambda), Azure ML, Azure Databricks, GCP
Data Stack: Databricks, Spark, PySpark, Delta Lake, Apache Iceberg, Lakehouse architectures