Solutions Architect

Position Title : Solutions Architect
Job Location:  Remote
Job Duration: 12 Months

Must have Skills/Attributes
AI, AWS, Enterprise Application, Java, LLM (Large Language Model), Python, RAG (Retrieval-Augmented Generation), Solution Architecture
 
Shift
9:00 a.m. to 5 p.m

Required Education/Experience: 
• Bachelor’s degree with 5+ years of experience in this capacity 

Required Qualifications: 
• This is Architecture position for AI related projects which require daily communications with business owners and other members of architecture team located in US

Position’s Contributions to Work Group: 
• As a Principal Digital Architect, you will own the end to end architecture solutions for complex systems—balancing scalability, performance, security, and rapid delivery—while influencing Enterprise technology strategy.
• This role requires strong technical depth, architectural judgment, and the ability to translate ambiguous business needs into durable, scalable solutions.

Typical task breakdown:
• Own and define solution and platform architectures for large scale, distributed systems from concept through production. 
• Create architecture that meets high standards for scalability, performance, resilience, and security. 
• Partner closely with business leaders, product owners, engineering managers, and delivery teams to ensure architectural alignment with business outcomes. 
• Assess, select, and introduce new technologies, including proof of concept development and architectural spikes. 
• Establish and enforce architectural standards, patterns, and best practices across platform teams. 
• Provide architectural guidance and mentorship to engineering teams, ensuring high quality implementation. 
• Ensure solutions meet security, compliance, and regulatory requirements. 
• Produce and maintain clear architecture documentation, including rationale and trade offs. 
• Continuously evolve platform architecture to improve developer productivity, system reliability, and cost efficiency.

Required Technical Skills: 
• Architectural Thinking: Ability to decompose complex problem spaces and develop pragmatic architecture options with clearly articulated trade offs. 
• Technical Leadership: Influence without authority; guide teams through architectural decisions and implementation challenges. 
• Communication: Clearly articulate complex technical concepts to both technical and non technical stakeholders. 
• Requirements Analysis: Translate business and non functional requirements into scalable technical designs. 
• Platform & Application Architecture: Strong foundation in designing modern application and platform architectures using established patterns and standards.

Consideration for top candidates:
• Experience defining AI reference architectures and standards for enterprise adoption. 
• Ability to explain and defend architectural trade offs between classical ML, LLM based approaches, and non AI solutions. 
• Proven experience taking AI systems from proof of concept to scaled production use. 
• Strong programming background in Python and Java, with the ability to reason at code level. 
• Proven experience designing and building enterprise scale, distributed systems. 
• Hands on experience with cloud native architectures, including AWS services, containerization, and orchestration (Docker, Kubernetes). 
• Deep understanding of data architecture: SQL and NoSQL databases, data warehouses (Snowflake specifically), data modeling, replication, and sharding. 
• Experience with modern DevOps practices: CI/CD, infrastructure as code, observability, and automated testing. 
• Strong API design experience (REST, GraphQL, gRPC), including versioning and documentation. 
• Ability to evaluate and introduce emerging technologies aligned to business goals. 

AI Related Skills:
• Hands on experience designing Retrieval Augmented Generation (RAG) architectures, including: 
• Data ingestion pipelines
• Document preprocessing and chunking strategies
• Vectorization and embedding models
• Query time retrieval, ranking, and context assembly
• Deep understanding of embedding techniques, similarity search, and trade offs across: 
• Vector dimensions
• Chunk size and overlap
• Latency vs. recall vs. cost
• Experience with vector databases and search layers (e.g., managed or self hosted vector stores) and their integration into application architectures.
• Experience with Agentic Frameworks
• Ability to architect end to end AI workflows, including: 
• Prompt design and prompt versioning
• Context management and memory patterns
• Model routing and fallback strategies
• Knowledge of LLM lifecycle considerations, including: 
• Model selection (hosted vs. self hosted)
• Fine tuning vs. RAG vs. hybrid approaches
• Evaluation, monitoring, and drift detection
• Strong understanding of AI system non functional requirements, including: 
• Performance and latency optimization
• Cost controls and token efficiency
• Security, data privacy, and guardrails
• Experience integrating AI capabilities into existing enterprise platforms via APIs and event driven architectures.
• Ability to assess, prototype, and productionize emerging AI technologies aligned to business use cases.