Artificial Intelligence Developer

2 weeks ago

lucknow, India beBeeAI Full time

Job Title: AI EngineerAbout Us:We are a leading provider of innovative AI solutions that help enterprises adopt AI responsibly.We protect AI workflows from data exposure, mitigate prompt vulnerabilities, and enforce compliance policy in real-time.Role DescriptionKey Responsibilities:Build production-grade services in the request/response path using Python/FastAPI with low latency, reliability, and observability.Model Development: Train, fine-tune, and evaluate ML models for classification, ranking, and anomaly detection; improve accuracy with solid feature engineering and error analysis.Efficiency & Reliability: Profile and optimize inference (CPU/GPU), use batching/quantization/ONNX where useful, and keep latency within SLOs (p50/p95).Pipelines & Data: Build robust data/ML pipelines for training and inference (Python + SQL), with versioned datasets, deterministic preprocessing, and schema validation.Experimentation: Design fair comparisons, select metrics (AUC/F1/PR-AUC/MAE), run ablations, and document decisions; automate tracking with MLflow/W&B (or similar).MLOps: Package and ship models/services using Docker and FastAPI; add health checks, logging/metrics, and CI/CD (GitHub Actions/Jenkins).Monitoring: Set up drift/performance monitoring, alerting, and shadow→enforce rollouts with safe rollback paths.Collaboration & Docs: Partner with backend/product teams to integrate models; write clear READMEs, design notes, and runbooks.Required Skills and Qualifications2–3 years of experience in Data Science, Applied ML, or LLM-based product development.Proficient in Python and comfortable writing SQL for data exploration or tagging workflows.Solid understanding of NLP concepts (Transformers, Attention, Tokenization, Fine-tuning).Hands-on experience with LLM APIs (OpenAI, Anthropic, etc.), LangChain, and vector databases (FAISS, Pinecone, Chroma).Exposure to evaluation and red-teaming of LLMs for safety or robustness.Basic familiarity with Streamlit, Dash, or simple HTML/JS for building internal tools.(Preferred) Experience deploying ML systems using Docker, FastAPI, or CI/CD pipelines in a startup or research setting.

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