Senior Data Engineer - Python

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