AI/ML Engineer

Job Description

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

• Should be able to interact with stakeholders, understand the problem statement, and come up with solutions

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to [email protected] learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills and Requirements

• Minimum 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 pre-trained models/ChatGPT were released

• Minimum 12+ years of total experience in Software Development, preferably with a Data Analyst/Data Scientist background

• 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.

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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.,)

• Some working experience with Cloud services, CI/CD and Microservices - Experience leading/mentoring junior data scientists or ML engineers is a plus

• Experience fine tuning SLMs is a huge plus