AI/ML Engineer

Overview

Remote
Depends on Experience
Contract - W2
Contract - 12 Month(s)

Skills

AI/ML Engineer
ML Engineer
AI Engineer
Healthcare
ChatGPT
Machine Learning
Artificial Intelligence
AI/ML

Job Details

Position: AI/ML Engineer

Location: Remote

Healthcare Client

Minimum qualifications:

  • 6+ years hands-on ML engineering (with significant ML experience prior to 2023): you ve shipped multiple ML systems to production. *Not looking for someone who started working on AI after the pre-trained models/ChatGPT were released*
  • Demonstrated production agent build (at least one end-to-end agentic framework delivered to users).
  • Strong with classical ML: feature engineering, cross-validation, calibration, regularization, class imbalance, interpretability (SHAP/LIME), time-series (forecasting, seasonality, drift).
  • Solid deep learning foundations (CNN/RNN/Transformers), and practical fine-tuning experience (e.g., LoRA/QLoRA, instruction tuning, RAG).
  • Proven MLOps: model registry/experiment tracking (MLflow or equivalent), model serving (FastAPI/TF-Serving/TorchServe/TGI/vLLM), observability.
  • Fluency in Python and the ML stack (NumPy/Pandas, scikit-learn, XGBoost/LightGBM, PyTorch/TensorFlow).
  • Excellent communication; can drive projects independently as an Individual Contributor.

Must have:

  • Experience with agent frameworks (LangGraph/LangChain Agents, AutoGen, Google ADK) and tool use (function calling, tool routing, planners).
  • Retrieval/RAG design: chunking strategies, embedding models, vector stores (FAISS, Pinecone, Weaviate), hybrid search, evals.
  • Must have implemented projects involving some classical ML problems(classification, clustering, regression, anomaly detection, time series etc.,)
  • A portfolio of production ML systems
  • A shipped agentic system used by real users.
  • Comfort acting as a self-directed Individual contributor who can turn vision into shipped systems

Responsibilities:

  • Own end-to-end ML/AI projects: problem framing, data pipelines, modeling, offline/online evals, deployment, monitoring, and iteration.

  • Build and productionize agentic workflows (tool-using/multi-step agents with retrieval, planning, and human-in-the-loop), including safety/guardrails and reliability.
  • Train classical ML models (tree ensembles, linear models, anomaly detectors, time-series forecasting) and deep learning models when appropriate.
  • Operationalize models with CI/CD, feature stores, reproducible training, and model registries
  • Monitor and improve live systems: data & concept drift detection, performance regression, bias/fairness, cost/latency; drive remediation playbooks.
  • Partner cross-functionally with product, data, and platform teams; write clear design docs
  • Work as an Individual contributor with minimal directions
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