Senior Data Science Engineer in India at Jobgether
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Job Description
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Science Engineer based in India.
This role sits at the convergence of advanced AI engineering, large-scale data systems, and cloud-native architecture, offering the opportunity to build next-generation intelligent platforms used in enterprise environments. You will design and optimize high-performance data pipelines, search systems, and retrieval architectures that power real-time analytics and AI-driven experiences. The position involves working deeply on Generative AI systems, including RAG pipelines and multi-agent frameworks, with a strong emphasis on production-grade reliability. You will also architect and deploy distributed solutions across major cloud providers while ensuring scalability, security, and performance. The environment is highly technical and innovation-driven, requiring strong ownership and the ability to operate across data, infrastructure, and AI layers. This is a hands-on engineering role with significant impact on system design and long-term platform evolution.
Design and optimize high-performance data, search, and AI systems across large-scale distributed environments, ensuring reliability, scalability, and efficiency.
- Architect and maintain ELK-based observability and search systems for real-time data ingestion, indexing, and analytics.
- Develop and optimize PostgreSQL databases, including advanced query design, indexing strategies, performance tuning, and data lifecycle management.
- Build and deploy production-grade Generative AI systems, including RAG pipelines and multi-agent orchestration frameworks.
- Design and implement AI agents capable of executing complex workflows using LLMs, embeddings, and tool-calling architectures.
- Deploy and manage cloud-native AI and data workloads across AWS, Azure, and GCP with a focus on security and scalability.
- Implement containerized MLOps workflows using Docker, CI/CD pipelines, and Infrastructure-as-Code practices.
- Collaborate with cross-functional teams to define system architecture, improve performance, and ensure production stability.
- Lead technical decision-making across data, AI, and infrastructure layers while mentoring engineers and driving best practices.
6+ years of experience in Python development with strong emphasis on production-grade engineering and scalable system design.
- Strong hands-on expertise with Elasticsearch/ELK Stack, including indexing, cluster management, and pipeline optimization.
- Advanced knowledge of PostgreSQL, including query optimization, schema design, and performance tuning.
- 3+ years of experience building and deploying Generative AI, NLP, or LLM-based production systems.
- Strong understanding of RAG architectures, vector embeddings, and multi-agent AI systems.
- Experience working with cloud platforms such as AWS, Azure, and GCP in production environments.
- Hands-on experience with Docker, CI/CD pipelines, and Infrastructure-as-Code tools.
- Strong problem-solving skills with the ability to debug complex distributed systems and data pipelines.
- Excellent communication skills with the ability to translate technical concepts into clear architectural decisions.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related quantitative field.
- Nice to have: experience with Spark, computer vision pipelines, or enterprise AI platforms (SageMaker, Vertex AI, Azure AI).
- Competitive compensation package aligned with experience and expertise.
- Hybrid work model with flexibility based in Bangalore, India.
- Opportunity to work on cutting-edge Generative AI and large-scale data systems.
- Exposure to multi-cloud environments and enterprise-grade architecture challenges.
- Learning and development opportunities in advanced AI, data engineering, and cloud technologies.
- Collaborative, high-impact engineering environment with strong ownership culture.
- Work on globally scaled systems impacting enterprise media and technology platforms.