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Managing Solution Architect
Chicago, IL
$150k - $220k
1 week ago
💻 Open SourceUses Cursor and AI assistants; mentions vibe coding as a plus and emphasizes hands-on Gen AI/LLM tooling.
About the Role
Capgemini is hiring a hands-on Managing Solution Architect to design and implement end-to-end cloud, data analytics, and AI platforms. The role focuses on technical leadership: coding, building prototypes, guiding development teams, and delivering production-grade, scalable AI and data solutions.
Job Description
Role
Managing Solution Architect focused on Cloud, Data Analytics, and AI. This is a highly technical, implementation-focused position responsible for designing architecture blueprints, selecting appropriate technologies, driving hands-on development, and guiding teams to deliver production-grade solutions.
Key Responsibilities
- Lead day-to-day coding, technical builds, and engineering delivery for end-to-end data and AI platforms.
- Partner with client delivery teams to execute complex platform and cloud migrations, and tech-stack modernization projects.
- Build and scale enterprise blueprints (e.g., Data Lake Medallion, event-driven, domain-driven, microservices) into functional application stacks.
- Create rapid technical prototypes and proof-of-concepts to support presales activities.
- Collaborate with technology alliance partners (AWS, Microsoft, Google, Snowflake, Databricks, Anthropic) to leverage platform features.
- Lead sprint teams from an engineering perspective, own deployment pipelines, environment configuration, and resolve critical development blockages.
Requirements
- Minimum 14 years of IT industry experience and at least 6 years in an architecture capacity.
- Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related field.
- Industry awareness across sectors such as Manufacturing, Automotive, Life Sciences, Telecommunications, Media, Hi-Tech, or Energy & Utilities is valued.
Technical Skills & Competencies
- Expert, hands-on programming with Spark, Scala, and Java in major hyperscaler environments (AWS, Azure, Google Cloud); professional cloud certifications preferred.
- Practical implementation experience with AI platforms and ecosystems such as AWS Bedrock, AWS SageMaker, Google Vertex AI, Azure ML, and OpenAI.
- Deep knowledge of LLM workflows: prompt engineering, fine-tuning, RAG, vector databases, agent architectures/Agent Mesh, model routing, orchestration, and related MLOps/LLMOps practices.
- Familiarity with AI developer productivity tools (e.g., Cursor, Codex, Claude Code) and technologies like LangChain, LlamaIndex, Pinecone, Milvus, PyTorch, TensorFlow, and transformer models (Claude, Gemini, OpenAI models).
- Strong data engineering experience with Python, Spark, BigQuery, Redshift, Synapse, Databricks, Snowflake, and experience with streaming/batch platforms (Glue, Data Factory, DataFlow, Composer, Airflow, Kafka, Flink).
- Experience with data governance tooling (Informatica, Alation, Collibra, Reltio), semantic layers, reusable data products, and secure data sharing.
- Hands-on experience with DevOps and delivery automation: GitHub Actions, Jenkins, Terraform, and git; familiarity with FinOps, infrastructure security, and resilient cloud architecture.
Compensation & Benefits
- Base compensation range: $150,000 - $220,000 (base for the posted location).
- Role may be eligible for additional variable incentives, bonuses, or commissions depending on position and local laws.
- Benefits (U.S. and Canada examples): paid time off by grade (vacation 12–25 days), company holidays, personal days, sick leave, medical/dental/vision coverage, retirement savings plans (e.g., 401(k), RRSP), life and disability insurance, employee assistance programs.