6 days ago
Base Salary
$150k - $220k/yr
Responsibilities
- Drive day-to-day coding, technical builds, and engineering delivery for end-to-end data and AI platforms.
- Design architecture blueprints and recommend technologies for complex client data, cloud, analytics, and AI challenges.
- Lead cloud migrations, platform migrations, and technology modernization projects with client delivery teams.
- Build prototypes, proof-of-concepts, enterprise blueprints, and implementation pricing engines for presales activities.
- Translate data lake, event-driven, domain-driven, and modular microservices patterns into production application stacks.
- Guide engineering teams, lead sprint teams, own deployment pipelines and environment configurations, and resolve critical development blockers.
- Collaborate with AWS, Microsoft, Google, Snowflake, Databricks, and Anthropic alliance technical leads.
Requirements
- At least 14 years of IT industry experience and at least 6 years of architecture experience.
- Bachelor’s or master’s degree in computer science, information systems, or a closely related technology field.
- Expert hands-on programming proficiency in Spark, Scala, and Java within AWS, Azure, or Google Cloud environments.
- Hands-on experience with AWS Bedrock, AWS SageMaker, Google Vertex AI, Azure ML, OpenAI, prompt engineering, LLM fine-tuning, agent systems, RAG, vector databases, MLOps, and LLMOps.
- Experience with Cursor, OpenAI Codex, Claude Code, LangChain, LlamaIndex, Pinecone, Milvus, PyTorch, TensorFlow, transformer models, model routing, LLM orchestration, and agent development.
- Strong data engineering experience with Python, Spark, BigQuery, Redshift, Synapse, Databricks, or Snowflake.
- Experience with relational and NoSQL database modeling; graph database knowledge is valuable.
- Experience developing microservices, event-driven platforms, and streaming or batch pipelines using tools such as Glue, Data Factory, Dataflow, Composer, Airflow, Kafka, or Flink.
- Experience with data governance, data quality, data lineage, observability, and data marshalling; Informatica, Alation, Collibra, or Reltio experience is preferred.
- Experience structuring semantic layers, reusable data products, secure data shares, resilient cloud architectures, infrastructure security controls, and FinOps scripting is valuable.
- Deep fluency in DataOps, DevOps, and delivery pipeline automation using GitHub Actions, Jenkins, Terraform, and Git.
- Active professional cloud certifications and industry experience in sectors such as manufacturing, automotive, life sciences, telecommunications, media, high tech, or energy and utilities are valued.
Benefits
- Comprehensive benefits package for regular full-time employees, including paid time off, vacation of 12–25 days depending on 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.
- Benefits vary according to local policy and eligibility.
Tech Stack
Amazon RedshiftApache AirflowApache FlinkApache KafkaApache SparkAWSAzureDatabricksGitGitHub ActionsGoogle BigQueryGoogle CloudInformaticaJavaJenkinsPythonPyTorchScalaSnowflakeTensorFlowTerraform
Categories
Solutions Engineering
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.