Solutions Architect

Required Qualifications:

·        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:

o   Experience defining AI reference architectures and standards for enterprise adoption.

o   Ability to explain and defend architectural trade offs between classical ML, LLM based approaches, and non AI solutions.

o   Proven experience taking AI systems from proof of concept to scaled production use.

o   Strong programming background in Python and Java, with the ability to reason at code level.

o   Proven experience designing and building enterprise scale, distributed systems.

o   Hands on experience with cloud native architectures, including AWS services, containerization, and orchestration (Docker, Kubernetes).

o   Deep understanding of data architecture: SQL and NoSQL databases, data warehouses (Snowflake specifically), data modeling, replication, and sharding.

o   Experience with modern DevOps practices: CI/CD, infrastructure as code, observability, and automated testing.

o   Strong API design experience (REST, GraphQL, gRPC), including versioning and documentation.

o   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.