Design reliable execution patterns covering retries, failure handling, state management, and external-system interactions.
Rapidly create prototypes and evolve successful prototypes into secure, maintainable, production-ready solutions.
Create technical documentation, examples, and reference implementations that simplify adoption by engineering teams.
Partner with domain engineers and cross-functional teams to translate complex engineering processes into reusable AI-enabled workflows.
Experience developing distributed applications using service-oriented or microservices architectures, message queues or event-driven patterns, caching, idempotency, retries, and consistency-aware design.
Required Qualifications
Strong software engineering experience with Python and hands-on development of backend services and APIs.
Experience developing Generative AI or LLM applications and production AI systems.
Hands-on experience with one or more agent or application frameworks such as LangChain, LangGraph, Pydantic AI, or equivalent.
Experience designing REST APIs, SDKs, or developer-facing libraries.
Understanding of distributed systems, asynchronous processing, workflow execution, and failure and retry patterns.
Experience integrating enterprise systems, developer tools, or engineering platforms.
Strong debugging and problem-solving skills across application and platform layers.
Experience with production observability, logging, telemetry, or tracing.
Ability to translate ambiguous engineering requirements into practical architectures and working implementations.
Strong experience with Elasticsearch, including mappings, analyzers, shards and replicas, aggregations, query DSL, index lifecycle management, performance tuning, and vector or hybrid search.
Hands-on experience designing and operating RAG systems, including retrieval evaluation, relevance measurement, grounding quality, and mitigation of hallucinations
Successful Candidate Characteristics
Strong hands-on engineering and analytical problem-solving ability.
Comfortable owning a problem from architecture and prototyping through productionization.
Able to work across AI, platform, infrastructure, and domain-engineering boundaries.
Comfortable learning new technologies and frameworks quickly.
Strong technical communication, documentation, and knowledge-sharing skills.
Able to balance rapid experimentation with production reliability and maintainability.
Collaborative and responsive when enabling other engineers to adopt platform capabilities.
Able to manage multiple priorities and deliver effectively in a cross-functional environment.
Minimum Qualifications
B.Tech/M.Tech in Computer Science, Electronics, or a related engineering discipline from a top-tier institution
Minimum Qualifications
- Bachelor's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related field.
- 3+ years of Data Science or related work experience.
Completed advanced degrees in a relevant field may be substituted for up to two years (Master’s = one year, Doctorate = two years) of work experience.
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