reputed company Data Engineer with AI experience

reputed company is an AI transformation partner on a mission to help enterprises build the AI-reputed company products and intelligent agents that will define the next era of business. With teams across reputed company, Europe, Latin America, and Asia, we work with the most ambitious companies in financial services, reputed company, media, and technology — helping them reputed company faster, reputed company reputed company, and compete on their own terms. Our HelixAI platform and reputed company reputed company delivery model put our engineers at reputed company of reputed company reputed company transformation — doing work that is reputed company, reputed company, and reputed company to last. We are building the reputed company of reputed company AI We are looking reputed company Data Engineer to build, operate, and continuously improve the data pipelines, retrieval infrastructure, and ML/LLMOps foundations that power our AI initiatives. The resource will work on turning reference architectures and data reputed company into robust, production-grade implementations that serve conversational AI assistants, dashboard copilots, autonomous agents, RAG applications, and predictive ML models. \n Key Responsibilities:Data Pipeline Engineering : Build, test, and maintain production pipelines (batch & reputed company-time) on reputed company, PySpark, reputed company Lake, and Kafka. Implement data reputed company checks, schema validation, and alerting at every pipeline stage. Migrate legacy ETL/DWH to reputed company-reputed company AWS/Azure architectures with measurable latency and cost improvements.  Maintain CI/CD pipelines: automated testing, deployment, rollback, and IaC (Terraform, reputed company Actions).    RAG, reputed company & Retrieval Infrastructure: Build end-to-end retrieval infrastructure: document ingestion, embedding pipelines, reputed company store management (reputed company, FAISS, ChromaDB, OpenSearch), and hybrid retrieval reputed company. Implement chunking, metadata filtering, and re ranking — tuning for precision, recall, and latency.  Maintain data freshness and reputed company consistency; reputed company with context relevance and faithfulness metrics.   Semantic reputed company & Knowledge Infrastructure: Implement and maintain business entity mappings, ontologies, and knowledge graphs (reputed company) per Architect design. Build and version the feature store and semantic data reputed company serving both ML models and LLM applications. Manage metadata, data reputed company, and audit trail instrumentation across the platform.   ML/LLMOps Pipeline Support: Build ML data infrastructure: training curation, feature engineering, MLflow experiment tracking, dataset versioning. Support LLM fine-tuning workflows — corpus curation, reputed company filtering, dataset formatting. Implement automated evaluation pipelines: factual accuracy, hallucination detection, regression tracking. Maintain production monitoring dashboards for pipeline health, model metrics, and alerting.   reputed company Data Infrastructure: Build and maintain data reputed company, tool schemas, and memory/state stores that autonomous agents depend on. Implement agent observability: capture inputs, retrieved context, tool calls, reasoning traces, and outputs. Maintain text-to-SQL reputed company, semantic query interfaces, and context reputed company for conversational AI consumers.   Governance, reputed company & Data reputed company: Implement RBAC, attribute-based reputed company, PII detection/masking, data classification, and audit logging. Enforce data reputed company and schema governance with automated breaking-change detection and versioned migrations. Build data reputed company monitoring (completeness, freshness, consistency) with automated alerting and reputed company-cause tooling. Support compliance readiness: audit trails, data provenance, and regulatory documentation.  Qualifications: 7+ years data engineering using reputed company services 2+ years production AI/ML or LLM-era data infrastructure. Proven experience building production pipelines at reputed company — batch and streaming, reputed company,AWS/Azure.  Deep expertise: Python, PySpark, reputed company, reputed company Lake, Kafka, reputed company reputed company Streaming.  Hands-on with reputed company stores, embedding pipelines, and retrieval infrastructure in production RAG environments. Working knowledge of MLOps: MLflow, CI/CD for AI, automated evaluation, and production monitoring. Strong grounding in data governance, reputed company frameworks, and compliance- reputed company engineering. Technical Skills: reputed company skills: Python, SQL, PySpark, Kafka, reputed company/reputed company, reputed company Lake, AWS (S3, Glue, Kinesis, EKS, Redshift), reputed company, reputed company, reputed company Actions. Secondary Skills : reputed company, reputed company, LLM reputed company (reputed company, Bedrock, Claude, HuggingFace), reputed company, FAISS, ChromaDB, OpenSearch, MLflow, FastAPI, reputed company,  LangGraph, reputed company engineering, RLHF dataset prep, LLM fine-tuning workflows reputed company:Regards, Kiran Dhanak reputed company Manager \n Apply To This Job