Managing Solution Architect

Managing Solution Architect

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you'd like, where you'll be supported and inspired by a collaborative community of colleagues around the world, and where you'll be able to reimagine what's possible. Join us and help the world's leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

** Location*
* Chicago, IL;
Atlanta, GA;
New York, NY;
Dallas, TX;
San Francisco, CA

** Your Role*
* We are seeking a highly technical, hands-on, and implementation-focused
** Solution Architect for Cloud, Data Analytics and AI
** In this role, your primary focus will be implementation and end-to-end technical execution. You will be the solution architect, designing architecture blueprints suggesting right fit technologies for solving complex technical asls from clients in areas of Data, Cloud and AI. You will be guiding team to write code, build prototypes, and directly guiding development teams to turn complex system designs into stable, scalable production grade solutions.

** Key Responsibilities*
* +  Drive the day-to-day coding, technical builds, and engineering delivery of end-to-end data and AI platforms, working closely with Capgemini's development team and clients .

+  Partner directly with client delivery teams to execute complex platform migrations, cloud migrations, and tech-stack modernization projects across Data, Analytics, and AI domains. Hands on Knowledge of Gen AI is must. The candidate must be able to use Claude Code, Open AI Codex and Cursor.

+ Build, scale, and realize detailed enterprise blueprints, translating conceptual patterns like Data Lake Medallion, event-driven, domain-driven, and modular microservices into functional application stacks.

+ Support presales activities by creating rapid technical prototypes, conducting proof-of-concept (POC) builds, and architecting specific implementation pricing engines based on deep technical realities are required.

+  Collaborate directly with technical leads from our alliance ecosystem-such as AWS, Microsoft, Google, Snowflake, Databricks, and Anthropic to know their latest features.

+ Lead sprint teams from an engineering perspective, taking full ownership of deployment pipelines, complex environment configurations, and the real-time resolution of critical blockages during development phases.

** Required skills and Expereince*
* +  Minimum of 14 years of experience in the IT industry.

+  Minimum of 6 years of dedicated experience acting in an Architecture capacity.

+ Industry awareness across one or more sectors is highly valued:
Manufacturing, Automotive, Life Sciences, Telecommunications, Media, Hi-Tech, or Energy & Utilities.

+ Bachelor's or master's degree in computer science, Information Systems, or a closely related technology field

** Technical Skills & Competencies*
* +  Expert-level, hands-on programming proficiency in Spark, Scala, and Java within major hyperscaler environments (AWS, Azure, or Google Cloud). Active, professional cloud certifications are highly desired.

+ Deep practical implementation experience with AI ecosystems like AWS Bedrock, AWS Sage Maker, Google Vertex AI, Azure ML, and OpenAI. Mastery of building functional pipelines featuring prompt engineering, LLM fine-tuning, Agent Mesh, RAG, Vector Databases, Chain-of-Thought, Context Engineering, Loop Engineering, and MLOps/LLMOps. Direct facility with AI developer productivity toolsets (e.g., Cursor, Codex, Claude Code). While vibe coding knowledge is good to have, hands on implementation knowledge of LLM fine tuning, context engineering, Vector Databases Design, Model Routing, LLM Orchestration, Developing Agents and building Agentic Mesh, technologies such as Lang Chain, Llama Index, Pine Cone, Milvus, PyTorch, Tensor Flow and different transformer models including Claude, Gemini, OpenAI models, and ability to optimize token usage etc.

are crucial for this role.

+ Strong data engineering experience with cross-platform analytical data warehouses and data lakes houses, specifically Python, Spark, Big Query, Redshift, Synapse, Databricks or Snowflake.

+  The candidate should have exposure to different styles of database modeling relational systems and modern No

SQL databases. Exposure or working knowledge in Graph database will be valuable.

+ Experience in developing microservices, event-driven platforms, and streaming/batch pipelines (Glue, Data Factory, Data Flow, Composer, Airflow, Kafka, Flink, or equivalent cloud services).

+  Experience in Data Governance tools for data quality, data lineage, observability, and data marshalling, with preference for practical tooling validation using Informatica, Alation, Collibra, or Reltio will be valuable.

+  Direct experience structuring real-world semantic layers, creating reusable data products, managing secure data shares, will be valuable.

+  Awareness about secure network layout, infrastructure security controls,…