Position Overview
We are seeking experienced Data/GenAI Engineers to join our Professional Services
team on a contract basis. You will work directly on client engagements delivering
production grade Generative AI solutions, including conversational AI assistants,
document processing automation, RAG (Retrieval-Augmented Generation) systems,
and AI-powered data analytics platforms. This role requires hands-on technical
execution, client interaction, and the ability to work independently within an agile
delivery framework.
Work timing - the candidate must be able to work from 8AM to 4PM EST.
Primary Responsibilities
GenAI Solution Development
• Design and implement production-ready Generative AI applications using
Amazon Bedrock, Anthropic Claude, and other foundation models
• Build and optimize RAG (Retrieval-Augmented Generation) pipelines with
vector databases (Weaviate, OpenSearch, Pinecone)
• Develop AI agents and multi-agent orchestration systems using frameworks
like LangChain, LlamaIndex, or custom implementations
• Create conversational AI interfaces with natural language understanding, intent
detection, and context management
• Implement prompt engineering strategies, few-shot learning, and fine-tuning
approaches for domain-specific applications
AWS Cloud Architecture & Development
• Build serverless architectures using AWS Lambda, API Gateway, Step
Functions, and EventBridge
• Design and implement data pipelines for AI model training, inference, and
feedback loops
• Develop RESTful APIs and WebSocket connections for real-time AI interactions
• Configure and optimize AWS services including S3, DynamoDB, RDS, SQS, SNS,
and CloudWatch
• Implement infrastructure-as-code using CloudFormation, CDK, or Terraform
Data Engineering & ML Operations
• Design and build data ingestion pipelines for structured and unstructured data
sources
• Implement ETL/ELT workflows for data preparation, cleaning, and transformation •
Create vector embeddings and semantic search capabilities for knowledge retrieval •
Develop data validation, quality monitoring, and observability frameworks • Optimize
model inference performance, latency, and cost efficiency Client Engagement &
Delivery
• Participate in sprint planning, daily standups, and client review sessions
• Translate business requirements into technical specifications and implementation
plans
• Provide technical guidance and recommendations to clients on AI/ML best
practices
• Document architecture decisions, code, and deployment procedures
• Troubleshoot production issues and implement solutions quickly
Required Technical Skills (Priority Order)
Tier 1 - Critical Must-Haves
• Amazon Bedrock - Hands-on experience with foundation models (Claude,
Nova, Llama or others), model invocation, streaming responses, and guardrails
• Agent Frameworks & Orchestration - Production experience with
LangChain, LlamaIndex, Bedrock Agents, or custom multi-agent orchestration
systems
• Python - Advanced proficiency with modern Python (3.9+), including
async/await, type hints, and testing frameworks (pytest, unittest)
• AWS Lambda & Serverless - Production experience building
event-driven architectures, function optimization, and cold start
mitigation
• Vector Databases - Practical experience with at least one: Weaviate,
OpenSearch, Pinecone, Chroma, or FAISS for semantic search
• LLM Integration - Direct experience with LLM APIs (Anthropic, OpenAI,
Cohere), prompt engineering, and response parsing
• API Development - RESTful API design and implementation using FastAPI, Flask,
or similar frameworks
Tier 2 - Highly Valuable
• Amazon Bedrock AgentCore - Experience with AgentCore Runtime,
Memory, Gateway, and Observability for building production agent
systems
• AWS API Gateway - Configuration, authorization, throttling, and integration
with Lambda/backend services
• DynamoDB - NoSQL data modeling, single-table design, GSI/LSI optimization,
and DynamoDB Streams
• AWS Step Functions - Workflow orchestration for complex AI pipelines and
multi step processes
• Docker & Containers - Containerization, ECR, ECS/Fargate deployment for
AI workloads
• Data Processing - Experience with Pandas, PySpark, AWS Glue, or similar
data transformation tools
Tier 3 - Strong Differentiators
• RAG Architecture - End-to-end RAG system design including chunking
strategies, retrieval optimization, and context management
• Embedding Models - Working knowledge of text embeddings (Bedrock
Titan, OpenAI, Cohere) and embedding optimization
• AWS S3 & Data Lakes - S3 event notifications, lifecycle policies, and data
lake architecture patterns
• CloudWatch & Observability - Logging, metrics, alarms, and distributed tracing
for AI applications
• IAM & Security - AWS security best practices, least privilege access,
secrets management (Secrets Manager, Parameter Store)
• CI/CD Pipelines - Experience with CodePipeline, GitHub Actions, or GitLab CI
for automated deployments
Tier 4 - Nice to Have
• SageMaker - Model training, deployment, endpoints, and feature stores •
OpenSearch - Full-text search, vector search, and hybrid search implementations
• EventBridge - Event-driven architectures and cross-service integrations •
WebSockets - Real-time bidirectional communication for streaming AI responses •
AWS CDK - Infrastructure-as-code using Python or TypeScript CDK constructs
• Fine-tuning & Training - Experience with model fine-tuning, PEFT methods,
or custom model training
Required Experience & Qualifications
• 5+ years of software engineering experience with at least 2+ years focused
on AI/ML, data engineering, or cloud-native development
• 2+ years of hands-on AWS experience with production deployments • 1+
years of direct Generative AI experience (LLMs, embeddings, RAG, agents)
• Proven track record delivering production AI applications from concept to
deployment
• Strong understanding of software engineering best practices (version control,
testing, code review, documentation)
• Experience working in agile/scrum environments with distributed teams
• Excellent problem-solving skills and ability to work independently with minimal
supervision
• Strong written and verbal communication skills for client-facing interactions
Preferred Qualifications
• AWS Certifications: Solutions Architect Associate/Professional, Machine
Learning Specialty, or Developer Associate
• Background in healthcare, financial services, or regulated industries
with understanding of compliance requirements (HIPAA, PCI-DSS, SOC 2)
• Contributions to open-source AI/ML projects or published technical content •
Experience with multi-tenant SaaS architectures and data isolation patterns
• Knowledge of cost optimization strategies for AI workloads (model
selection, caching, batching)
• Familiarity with frontend frameworks (React, Angular) for building AI-powered UIs
Project Examples You May Work On
• Building conversational AI assistants for customer service automation using
Bedrock and Anthropic Claude
• Implementing RAG systems for document processing, classification, and intelligent
search
• Developing AI-powered data extraction and validation pipelines for healthcare
claims processing
• Creating multi-agent systems for complex workflow automation and decision
support
• Building integration marketplaces connecting AI capabilities to third-party platforms
• Designing voice AI solutions using Amazon Connect and Polly for
customer engagement
• Implementing AI-driven content recommendation and personalization engines