Job Description
Details:
- Contract Duration: Min 3–6 months
- Work Timing: 8:00 AM – 4:00 PM EST
- Start Timeline: Within 2 weeks
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.
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.
