8 days ago
Base Salary
$82k - $193k/yr
Responsibilities
- Translate business requirements into scalable, secure, high-performance AI architectural blueprints.
- Write clean, modular, maintainable production code for custom AI solutions, pipelines, and integrations.
- Tune, quantize, and prune models to meet performance thresholds.
- Evaluate and select LLMs, SLMs, and traditional ML models based on latency, cost, and accuracy.
- Design machine learning infrastructure across on-premises, cloud-hybrid, and multi-cloud environments.
- Develop RESTful APIs and data pipelines and integrate AI services with enterprise software ecosystems.
- Use model versioning, monitoring, containerization, and orchestration practices for production AI systems.
- Document architectures, data flows, and API specifications while maintaining secure AI development and data privacy standards.
- Lead development efforts, mentor junior staff, and promote code quality and model security.
- Work within an Agile/Scrum framework and deliver against frequent milestones.
Requirements
- 10+ years of experience designing, developing, and delivering enterprise-scale technology solutions, including AI/ML architectures.
- Proven ability to translate business requirements into secure, scalable, high-performance AI platforms.
- Experience designing scalable machine learning infrastructure in on-premises, cloud-hybrid, or multi-cloud environments.
- Expertise with Python-based frameworks such as PyTorch, TensorFlow, LangChain, and LlamaIndex.
- Hands-on experience with AI CI/CD, model versioning, and monitoring tools such as MLflow, Kubeflow, and Weights & Biases.
- Strong RESTful API development and data pipeline architecture experience using technologies such as FastAPI, Flask, Apache Airflow, Kafka, and Spark.
- Deep understanding of neural networks and transformer architecture, including model optimization and loss-function principles.
- Strong proficiency in Python and SQL, with familiarity with C++ or Go for high-performance components.
- Expertise with Docker and Kubernetes for containerization and orchestration.
- Experience with vector databases such as Pinecone, Milvus, and Weaviate, as well as traditional relational and NoSQL databases.
- Ability to integrate AI services with security, compliance, and data governance frameworks.
- Strong mentoring, documentation, secure development, and enterprise delivery capabilities.
Benefits
- Hybrid work arrangement in Long Beach, California.
- Comprehensive non-negotiable benefits package for regular full-time employees, subject to local policy and eligibility.
- Paid time off, including 12–25 vacation days depending on employee grade, company holidays, personal days, and sick leave.
- Medical, dental, and vision coverage, or provincial healthcare coordination in Canada.
- Retirement savings plans such as 401(k) in the U.S. and RRSP in Canada.
- Life and disability insurance and employee assistance programs.
Tech Stack
Apache AirflowApache KafkaApache SparkC++DockerFastAPIFlaskGoKubernetesMLflowPythonPyTorchSQLTensorFlow
Categories
About Capgemini
Capgemini is a global IT services and consulting firm that delivers strategy, cloud, AI, software engineering, and managed services to large enterprises and public-sector clients. Founded in 1967 and headquartered in Paris, it is publicly traded on Euronext Paris and operates in 50+ countries. The group expanded its engineering capabilities by acquiring Altran in 2020, now operating as Capgemini Engineering.