Job Post : Senior Data Engineer

Experience : 7+ years

Timing : Approx. 5 : 30 PM/6 : 30 PM IST to start, then 8 hours a day (Basically EST time zone)

Location : Remote (India)

Position Overview :

We are seeking experienced Data/GenAI Engineers to join our Professional Services team. 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.

- Design and implement production-ready Generative AI applications using Amazon Bedrock, Anthropic Claude, and other foundation models

- Build and optimise 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

- 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 optimise 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

- Optimise 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

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 optimisation, 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, authorisation, throttling, and integration with Lambda/backend services

- DynamoDB - NoSQL data modelling, single-table design, GSI/LSI optimisation, 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 :

- RAG Architecture - End-to-end RAG system design including chunking strategies, retrieval optimisation, and context management

- Embedding Models - Working knowledge of text embeddings (Bedrock Titan, OpenAI, Cohere) and embedding optimisation

- 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 :

- 7 to 8+ years of software engineering experience with at least 2/3+ years focused on AI/ML, data engineering, or cloud-native development

- 2-3+ years of hands-on AWS experience with production deployments

- 1-2+ 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