We are seeking a visionary and execution-focused Senior Data Science Enginee r to spearhead the design, development, and scaling of our next-generation intelligent systems. This role is built for a technical heavyweight who sits at the intersection of Advanced Generative AI (RAG Multi-Agent Systems), High-Performance Data Architectures, and Enterprise Cloud Engineering.

You will own the optimization of our transactional and search layers (ELK PostgreSQL), build resilient orchestration pipelines, and deploy secure, hybrid-cloud solutions across AWS, Azure, and GCP.

What Youll Do

High-Performance Data Search Architecture

Enterprise Log Search Analytics : Architect, configure, and maintain the ELK Stack (Elasticsearch, Logstash, Kibana) to ingest, parse, index, and visualize massive multi-source data streams, enabling real-time observability and sub-second semantic/lexical search.

PostgreSQL Optimization : Act as the subject matter expert for the relational database layer. Write complex queries, stored procedures, functions, and triggers. Diagnose bottlenecks, execute query optimization, index tuning, and oversee database backup, recovery, and archiving strategies.

AI Engineering Agentic Systems

Autonomous Agents Tool Integration : Design, productize, and mature task-oriented AI agents and multi-agent orchestrations that execute complex, deterministic workflows rather than simple conversational chat completions.

Production RAG Architectures : Build and optimize end-to-end Retrieval-Augmented Generation (RAG) pipelines using advanced chunking, metadata tagging, hybrid search, and reranking strategies over massive structured and unstructured datasets.

Model Evaluation Frameworks : Deploy and monitor Large Language Models (LLMs) and Large Multimodal Models (LMMs) for text, image, audio, and video analytics. Productize solutions using enterprise-grade frameworks (e.g., LangChain, Langgraph, Semantic Kernel, or custom orchestration runtimes).

Cloud-Native Deployment Infrastructure

Multi-Cloud Excellence : Design and deploy highly available, secure, and compliant AI workloads across AWS, Azure, and GCP.

MLOps Containerization : Enforce strict containerization using Docker and automate pipeline orchestration. Collaborate with DevOps to implement secure, automated CI/CD infrastructures and infrastructure-as-code (IaC).

Leadership Iterative Delivery

Technical Ownership : Take end-to-end ownership of major features and core architecture components, driving technical decisions from concept to production.

Agile Mentorship : Embrace an iterative, fast-paced development culture. Mentor junior engineers, conduct robust code reviews, and advocate for engineering best practices.

What Were Looking For

Technical Core

Python Mastery: 6+ years of professional experience in modern Python development with a strict emphasis on writing clean, maintainable, production-ready code.

Data Layer Expertise : 4+ years of deep hands-on experience with Elasticsearch/ELK Stack (cluster management, pipeline routing, and indexing) and PostgreSQL (advanced query optimization, performance tuning, structural design).

Generative AI NLP : 3+ years of experience putting deep learning, NLP, or LLM-based solutions into enterprise production. Solid understanding of embedding spaces, vector databases, and agentic tool-calling patterns.

Cloud DevOps : Demonstrated proficiency in provisioning and managing resources within AWS, Azure, and GCP . Solid experience with Docker and production-grade orchestration tools.

Professional Cognitive

Problem Solving : Exceptional analytical and critical-thinking skills; ability to debug highly distributed systems and complex data pipelines.

Communication : Strong communication skills with the ability to articulate architectural decisions to internal stakeholders and translate complex customer requirements into engineering milestones.

Education : Bachelor s or Master s degree in Computer Science, Data Science, Engineering, or a highly quantitative field.

Preferred Future-Forward Skills

Experience building or deploying high-performance Computer Vision pipelines (object detection, multi-object tracking, or multimodal video analytics).

Familiarity with distributed computing environments such as Apache Spark .

Exposure to cloud-native machine learning development suites (such as Azure AI Foundry, AWS SageMaker, or Google Vertex AI) with an emphasis on governance and enterprise security models.

Passion for tracking the frontier of AI, specifically moving from standard LLM prompts to fully autonomous, self-correcting agent architectures.

Office Timing: 1pm to 9pm IST

Office Location: Manyata Tech Park, Bangalore, India

Work Model: Office/Hybrid

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

Senior Data Science Engineer

Job Description

No Referrers Available