Onsite: AI Architect with AWS Bedrock in CA.

[US IT Recruiter]

Hello All,

I have an exciting opportunity as an AI Architect with AWS Bedrock with one of our clients. If you are interested in this opportunity, please share your updated Resume to proceed further. 

Mandatory Skills : Architectural diagrams

Job Title: AI Architect with AWS Bedrock

Location: Torrance, CA (3 days onsite)

Hire Type: Contract  

Duration: Long Term

Client: LTIM

 

Job Description:

AI Architect with AWS bedrock, langchain, RAG, GraphQL, PostgreSQL, Tensorflow, PyTorch

 

Job Details:

• Design multilayered AI solutions balancing compute efficiency contextual fidelity and algorithmic adaptability retrieval reasoning planning tooluse
• Develop advanced RAG pipelines leveraging vector databases eg ChromaDB Milvus FAISS and embedding strategies for contextual accuracy
• Integrate AI capabilities with enterprise systems via REST and GraphQL APIs ensuring secure and scalable interoperability
• Establish best practices for algorithm selection and layering combining neural models symbolic reasoning and toolbased agents for optimal performance
• Collaborate with crossfunctional teams to embed AI agents into business workflows and align with compliance and governance standards
• Implement structured output validation and schema enforcement using Pydantic FastAPI and JSON Schema for robust data integrity
• Optimize compute resources and latency tradeoffs across cloud hybrid and edge environments for highperformance AI workloads
• Define observability baselines telemetry tracing evaluation metrics and rollout strategies for safe iterative deployments

Required Skills

• Programming Architecture Python 310 Async design modulardistributed architecture microservices
• LangChain Ecosystem LangChain LangGraph prompt templates agent orchestration patterns
• AIML Frameworks OpenAI API HuggingFace Transformers TensorFlow PyTorch experience with finetuning and inference optimization
• Data Context Management SQLAlchemy PostgreSQL JSON Schema Mapping feature engineering and contextual pipelines
• Vector Databases Semantic Search ChromaDB Milvus FAISS embedding optimization and similarity search strategies
• Algorithmic Design Strong understanding of algorithm layering retrieval reasoning planning hybrid AI approaches and computeaware model selection
• API Integration Security REST GraphQL OAuth enterprisegrade security practices
• DevOps CICD Git Docker Azure DevOps or equivalent FastAPI Uvicorn containerized deployments and automated pipelines
• Agentic Capabilities Reasoning adaptation tool calling MCPbased solutions ReAct agents Supervisormultiagent coordination
• Performance Optimization Distributed compute strategies GPUTPU utilization quantizationpruningdistillation caching batching

 

Thanks,
devender....@gmail.com