Data/GenAI Engineer

Data/GenAI Engineer Senior Level (6 - 8 Years of Experience)
● Experience: 6-8 years
● Allocation: Remote
● Payment Terms: 45 Days
● Position open: 02
● Working time:EST (From 5:30PM or 6PM IST until 2AM to 3AM)
● Tenure: 3-6 months
● Onboarding: 2 weeks
Additional Details Require: NA
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):
Tire 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 Have Worked 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.