AI/ML Engineer RAG & Retrieval Systems (Kolkata)

About the job: Company Overview WBE Consultants LLC is a US-based technology and consulting firm specializing in enterprise digital transformation, with a focus on SAP S/4HANA migrations. Our India development arm, Platinum Consulting & IT Solutions Pvt Ltd, is responsible for building our flagship products. Our product suite includes AMIGO (AI Managed Implementation Governance Office), a Salesforce-native project governance platform, and Belden, an AI-powered project intelligence agent that provides health analysis, risk intelligence, automated reporting, and decision support for complex enterprise programs. The Opportunity We are looking for an AI/ML Engineer to join our team building Beldens AI engine. You will work alongside a Senior AI/ML Engineer, contributing to the development, testing, and optimization of our RAG (Retrieval-Augmented Generation) pipeline on AWS. Belden is built entirely on AWS (Bedrock, Lambda, S3, Pinecone) and serves as the intelligence layer for AMIGOs Salesforce-based governance data. The core technical challenge is building a production-grade RAG pipeline that can accurately retrieve and reason over deeply hierarchical, relational business data. This is an excellent opportunity for someone with foundational AI/ML experience who wants to go deep on RAG systems and work on a genuinely hard problem making retrieval work over complex enterprise data structures. Youll learn from experienced engineers while contributing meaningfully to a commercial product. Key ResponsibilitiesData Pipeline Development Build and maintain data transformation pipelines that convert Salesforce JSON into embedding-ready formats Implement chunking logic that creates self-contained, contextually rich documents from hierarchical data Develop and test Lambda functions for data ingestion, transformation, and retrieval Maintain incremental sync processes between Salesforce (via S3) and Pinecone Retrieval & Evaluation Execute retrieval quality tests and document results Build and maintain evaluation datasets (query-answer pairs with ground truth) Implement automated testing pipelines for retrieval accuracy Analyze retrieval failures and propose improvements to the senior engineer Experiment with embedding models, chunking strategies, and reranking approaches AWS Infrastructure Support Configure and maintain Bedrock knowledge bases and agent components Monitor Lambda performance, costs, and error rates Implement logging and observability for pipeline debugging Support deployment and testing across development and production environments Prompt Engineering & Testing Develop and refine prompt templates for Beldens five core topics Test prompt variations and document which approaches produce better outputs Implement guardrails and scope controls to prevent out-of-domain responses Create test suites for regression testing prompt changes Collaboration & Documentation Work closely with the Salesforce development team on data format requirements Document pipeline configurations, test results, and operational procedures Participate in code reviews and architecture discussions Communicate progress and blockers clearly to the team Required QualificationsExperience 24 years in software engineering with exposure to AI/ML, NLP, or data engineering Hands-on experience with at least one RAG or LLM-based project (production or significant prototype) Familiarity with the RAG pipeline concept: embedding vector store retrieval generation Technical Skills Python: Strong proficiency this is your primary working language for Lambda functions and data pipelines AWS Fundamentals: Working knowledge of S3, Lambda, IAM basics, CloudWatch logs Vector Databases: Familiarity with Pinecone, Weaviate, or similar (experience with any vector DB is acceptable) LLM APIs: Experience calling LLM APIs (OpenAI, Anthropic, Bedrock, or similar) and handling responses Data Transformation: Comfortable working with JSON, handling nested structures, and writing transformation logic Core Competencies Curiosity about how things work you dig into why something failed, not just that it failed Attention to detail retrieval quality depends on careful implementation Clear written communication youll document findings and explain technical issues to the team Willingness to learn RAG is a fast-evolving field; you should enjoy staying current Preferred Qualifications AWS Bedrock experience: Familiarity with Bedrock agents, knowledge bases, or model invocation Pinecone specifically: Experience with Pinecone indexing, querying, and metadata filtering Evaluation frameworks: Experience with RAG evaluation tools (RAGAS, TruLens, or custom evaluation pipelines) Prompt engineering: Demonstrated ability to craft prompts that produce consistent, well-structured outputs Salesforce or CRM data: Familiarity with Salesforce object structures or similar CRM/ERP data models LangChain or similar: Experience with LLM orchestration frameworks (helpful for understanding patterns, though we use custom code) Who can apply: Only those candidates can apply who: • have minimum 2 years of experience • are Computer Science Engineering students Salary: Competitive salary Experience: 2 year(s) Deadline: 2035-01-01 00:00:00