Solutions Architect

Hi,
We are having an immediate requirement for the below mentioned role:
Solutions Architect (Principal Digital Architect AI / GenAI)
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Job Summary

We are looking for an experienced Principal Digital Architect AI / GenAI to lead the end-to-end architecture of complex enterprise AI and digital platforms.

The ideal candidate will have strong experience designing large-scale distributed systems, AI/GenAI architectures, RAG solutions, Agentic AI workflows, cloud-native platforms, APIs, data architecture, DevOps, and enterprise integration.

This position requires regular collaboration with business owners, product teams, engineering leaders, and architecture teams located in the U.S.

Experience Required

• Bachelor's degree with 5+ years of relevant experience required

• Preferred: 10 15+ years of overall IT experience

• Strong experience in Solution Architecture, Application Architecture, Platform Architecture, or Enterprise Architecture

• Hands-on experience architecting and productionizing AI/ML and Generative AI solutions

Key Responsibilities

• Own and define end-to-end solution and platform architecture for enterprise-scale AI and distributed applications from concept through production.

• Design highly scalable, secure, resilient, performant, and cost-efficient architectures.

• Translate complex and ambiguous business requirements into durable technical solutions.

• Collaborate with business leaders, product owners, engineering managers, developers, and architecture teams.

• Define and maintain AI reference architectures, architectural patterns, standards, and best practices.

• Evaluate new technologies and conduct proofs of concept, prototypes, and architectural spikes.

• Guide engineering teams through architectural decisions, design reviews, and implementation challenges.

• Evaluate architectural trade-offs across traditional software, classical Machine Learning, LLM-based solutions, RAG, and Agentic AI.

• Ensure architectures comply with enterprise security, privacy, regulatory, and governance requirements.

• Produce architecture diagrams, design documentation, decision records, and technical trade-off analysis.

• Improve platform reliability, developer productivity, observability, scalability, and infrastructure cost efficiency.

• Mentor technical teams and provide architectural leadership across multiple initiatives.

Required Technical Skills

• Strong Solution Architecture / Platform Architecture / Application Architecture expertise

• Enterprise-scale distributed systems architecture

• Strong programming knowledge in Python and Java

• Cloud-native architecture, preferably AWS

• Docker and Kubernetes

• Microservices and event-driven architectures

• Strong API architecture experience with:

  • REST

  • GraphQL

  • gRPC

• API versioning, integration, security, and documentation

• Strong understanding of:

  • SQL

  • NoSQL databases

  • Data modeling

  • Replication

  • Sharding

  • Data warehouses

  • Snowflake

• Modern DevOps practices including:

  • CI/CD

  • Infrastructure as Code

  • Automated testing

  • Observability

  • Monitoring

AI / Generative AI Architecture Skills

Strong hands-on experience designing enterprise Generative AI and RAG architectures, including:

• Data ingestion pipelines

• Document preprocessing

• Chunking strategies

• Embedding generation

• Vectorization

• Similarity search

• Query-time retrieval

• Ranking and reranking

• Context assembly

• Vector databases and semantic search platforms

Must understand RAG design trade-offs involving:

• Embedding dimensions

• Chunk size

• Chunk overlap

• Retrieval quality

• Recall

• Latency

• Performance

• Token consumption

• Cost optimization

Agentic AI & LLM Skills

• Experience with Agentic AI frameworks and multi-step AI workflows

• Prompt engineering and prompt versioning

• Context management

• Conversation memory and AI memory patterns

• Model routing

• Model fallback strategies

• LLM orchestration

• AI workflow integration with enterprise systems

• API and event-driven AI integrations

Strong understanding of:

• Hosted vs. self-hosted LLMs

• Fine-tuning vs. RAG vs. hybrid approaches

• Model selection

• LLM evaluation

• AI monitoring

• Drift detection

• Guardrails

• AI security

• Data privacy

• Performance and latency optimization

• Token efficiency

• AI cost controls

Preferred Qualifications

• Experience defining enterprise AI reference architectures and standards

• Proven experience taking AI solutions from POC/prototype to enterprise-scale production

• Experience assessing and adopting emerging AI technologies

• Strong architectural decision-making and trade-off analysis skills

• Ability to influence technical teams without direct authority

• Excellent communication skills with both technical and non-technical stakeholders

• Ability to communicate architectural recommendations clearly to U.S.-based business and technology teams

Key Skills / Keywords

Principal Architect, Digital Architect, AI Architect, GenAI Architect, Solution Architect, Enterprise Architect, RAG, Generative AI, LLM, Agentic AI, Python, Java, AWS, Kubernetes, Docker, Snowflake, Vector Database, Embeddings, Semantic Search, REST API, GraphQL, gRPC, Microservices, Distributed Systems, SQL, NoSQL, CI/CD, Infrastructure as Code, Observability, AI Governance, AI Security