Gen Ai Engineer

Engagement Details:

• Contract Duration: Min 36 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.