Software Engineer I - AI and ML

WHAT YOU'LL DO:

• Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction.
• Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring.
• Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable.
• Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments.
• Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems.
• Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products.

WHAT YOU'LL NEED:

• Python: Strong hands-on proficiency for building, scripting, and deploying AI/ML systems.
• NumPy · Pandas · FastAPI · Scikit-learn
• Machine Learning: Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.
• PyTorch · TensorFlow · XGBoost · DBSCAN
• Generative AI (2+ yrs): Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features.
• LLMs · RAG · Prompt Eng. · Fine-tuning
• NLP & Search / Ranking: Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models.
• spaCy · BERT · FAISS · Elasticsearch
• API Development: Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.
• REST · FastAPI · OAuth2 · Swagger
• Databases: Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.
• PostgreSQL · MongoDB · Vector DBs
• GOOD TO HAVE: Cloud Platforms: Deploys and scales AI workloads on AWS, Azure, or GCP.
• AWS · Azure
• TypeScript / JavaScript: Frontend or full-stack exposure for building ML-powered product interfaces.
• TypeScript · React · Node.js
• MLOps: Manages the ML lifecycle — tracking, versioning, and pipeline automation.
• MLflow · Kubeflow · CI/CD
• Containerization & Orchestration: Packages and scales AI services using containers and cluster management.
• Docker · Kubernetes