Job Summary

Overview Why we are looking for you: You have a passion for AI and platform engineering. You have a deep understanding of AI, not just applied conversational AI but the mechanics of a working AI system in production. You can operate as a senior technical authority who connects enterprise AI strategy, platform architecture, governance, and delivery execution. You are comfortable solving ambiguous, cross-business-unit problems where reusable AI platforms, assets, and standards must scale beyond a single use case. You have successfully delivered AI agents with measurable business benefit. What you will enjoy in this role: A senior individual contributor role with high visibility across product, engineering, data, security, finance, and business partners. Exposure to a range of top-notch enterprise scale AI solutions. Access to a global user base and all internal and external solutions.

Ownership for building platforms and solutions of strategic importance for Epsilon. Working with top AI talent that has delivered applied AI solutions at scale. Prospect to shape enterprise-wide AI architecture patterns, reusable accelerators, governance controls, and platform standards adopted across teams.

Responsibilities - Understand end-user requirements and expectations from AI solutions; translate to effective architecture and design. - Deliver platforms that enable effective and secure use of AI, specifically through improved Cost management, Governance and Discoverability. - Build interoperability across hyper-scalers and agent development tools to standardize AI spend management and governance. - Lead architecture and technical design for reusable AI platforms,



agent frameworks, RAG pipelines, evaluators, orchestration services, APIs, and integrations with enterprise systems. - Define reference architectures, engineering standards, guardrails, and delivery patterns that improve reliability, observability, security, scalability, and cost efficiency of AI solutions. - Guide build-versus-buy decisions, model and platform selection, and architectural trade-offs across foundation models, cloud AI services, open-source frameworks, and enterprise tooling. - Establish evaluation, testing, monitoring, feedback-loop, and runbook practices for production AI systems, including hallucination risk, quality benchmarks, latency, token usage, and operational health. - Partner with business units to assess AI opportunities, validate feasibility, define measurable success criteria, and convert promising use cases into scalable platform capabilities. - Mentor engineers and influence technical execution across teams through design reviews, code quality standards, reusable components, documentation, and hands-on engineering guidance. - Work closely with AI engineers in team to ensure high quality, reliable solutions and platforms.

Qualifications

- 12-18 years of experience building enterprise scale web applications.
- Grounded,



hands-on experience building Generative AI solutions, ML solutions, MLOps pipeline, and building agentic AI workflows, preferably in Microsoft tech stack.
- Strong hands-on engineering depth in Python and modern backend architectures, with the ability to design scalable APIs, services, integrations, and reusable libraries for AI applications.
- Deep understanding of LLM application patterns including RAG, prompt orchestration, tool/function calling, structured outputs, agentic workflows, multi-agent orchestration, and human-in-the-loop controls.
- Experience with AI platform capabilities such as vector databases, feature stores, model serving, evaluation frameworks, observability, tracing, usage metering, and cost optimization.
- Working knowledge of cloud AI and data platforms such as AWS Bedrock, Azure OpenAI, Azure AI Foundry, Databricks, containerized services, serverless patterns, and cloud-native deployment approaches.
- Ability to embed responsible AI practices including security, privacy, RBAC, model governance, auditability, compliance controls, restricted-topic handling, and production-readiness reviews.
- Proven ability to influence senior engineers, architects, product leaders, and business stakeholders through clear technical communication, decision frameworks, and outcome-driven architecture leadership.

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.

📌 Sr Staff Software Engineer (Bengaluru)
🏢 Epsilon Data Management
📍 Bengaluru