Engineer end-to-end ML pipelines —data ingestion, feature engineering, training, hyper-parameter optimization, evaluation, registration and automated promotion.
Harden research code into production-grade micro-services, packaging models in Docker/Kubernetes and exposing secure REST, gRPC or event-driven APIs for consumption by downstream applications.
Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency.
Optimize performance and cost at scale —selecting appropriate algorithms, applying quantization/pruning, and tuning GPU/CPU auto-scaling policies to meet strict SLA targets.
Instrument comprehensive observability —real-time metrics, distributed tracing, drift & bias detection and user-behavior analytics.
Embed security and responsible-AI controls in partnership with Security, Privacy and Compliance teams.
Contribute reusable platform components —feature stores, model registries, experiment-tracking libraries.

Requirements

3-5 years in AI/ML and enterprise software.
Comprehensive command of machine-learning algorithms — regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques.
Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel).
Proficiency in Python and Java; containerization (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines).
Strong business-case skills—able to model TCO vs. NPV and present trade-offs to executives.
Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.

Benefits

Health insurance
Retirement plans
Paid time off
Professional development opportunities

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard skills

machine learning algorithmsregressiontree-based ensemblesclusteringdimensionality reductiontime-series modelsdeep learning architecturesCNNsRNNstransformers

Soft skills

business-case skillsstakeholder managementcommunication