AI/ML Engineer

AI/ML Engineers - 6+ years of experience

New :

Own end-to-end ML model development and productionization — from problem framing and model building to deployment, monitoring, and scaling.

 • Design and build ML models (classification, regression, anomaly detection, forecasting, NLP/LLM-based, as applicable) to solve defined business problems

 • Build CI/CD pipelines for model training → validation → deployment

 • Containerize and serve models (batch + real-time inference)

 • Set up monitoring for drift, latency, and data quality

 • Collaborate with business/product stakeholders on requirements and data science on model handoff and reproducibility

 • Own rollback/versioning strategy and A/B or shadow testing

Tech stack

 • Serving: FastAPI/TorchServe/Triton

 • Orchestration: Kubernetes, Docker, Airflow/Kubeflow

 • CI/CD: GitHub Actions/GitLab CI, ArgoCD

 • Monitoring: Prometheus/Grafana, Evidently/WhyLabs for drift

 • MLOps framework: MLflow, Weights & Biases, or DVC

 • Languages: Python (primary)

Old:-

Develop and deploy AI/ML models — including NLP, speech, and predictive models — that power client solutions within a Delivery POD, working closely with Data Engineers and MLOps to bring models into production.

•      Design, train, and evaluate ML models addressing the POD's client use cases

•      Collaborate with Data Engineers to build feature pipelines and ensure data quality for models

•      Partner with MLOps to productionize and monitor models in live client environments

•      Continuously improve model performance based on production feedback and business metrics

•      Document model design decisions, assumptions, and limitations for governance and audit purposes

•      Stay current with advances in AI/ML relevant to the POD's domain (NLP, speech, forecasting, etc.)

•      Strong experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)

•      Proficiency in Python and experience with model development lifecycle end-to-end

•      Experience with NLP, speech, or LLM-based systems (as relevant to the POD)

•      Understanding of model evaluation, validation, and performance tuning

•      Familiarity with MLOps practices for deploying and monitoring models in production

•      Experience building ML models for financial use cases (credit risk, fraud detection, forecasting, document intelligence)

•      Understanding of model explainability and governance requirements in regulated environments

•      Awareness of bias, fairness, and compliance considerations for financial decisioning models

•      Experience with LLMs, RAG systems, or conversational AI

•      Experience building models for Indian languages or speech recognition

B.Tech/M.Tech in Computer Science, Data Science, or related field.