• Design and build AI agent pipelines and maintain RAG systems • Integrate and optimize Large Language Models and structured output workflows • Design, develop, and deploy product features and scalable backends • Collaborate with clients and stakeholders to translate business requirements into software solutions • Own observability and quality metrics for AI systems

• Build and maintain the services, data models, and APIs that power the platform — designed for correctness, testability, and scale. • Work on the systems that coordinate complex, multi-step interactions between AI agents and external systems, improving their reliability and throughput. • Design systems that grade agent outputs, combining deterministic checks with model-assisted judgment — and make scoring reliable, explainable, and reproducible. • Build pipelines that generate, transform, and quality-check large volumes of structured data and benchmark content. • Add the tests, instrumentation, and safeguards needed to trust outputs from systems that are inherently non-deterministic.

Role Description As a fast-growing scale-up, our team of 100+ tech specialists—including Data Engineers, Data Scientists, and more—delivers innovative analytics solutions across industries like FMCG, retail, manufacturing and FSI. Together with a partnering company, we're looking for a strong Software Engineer to build the systems behind an AI agent evaluation platform — systems that test, grade, and stress AI agents at scale, and turn the results into actionable signal about how those agents behave. This is a software engineering role first — designing services, data models, and pipelines that have to be correct, tested, and maintainable — applied to an AI problem domain. You'll work with LLMs regularly, but the core of the job is engineering, not prompt-tuning. What Your Day Might Look Like: - Backend services & APIs: Build and maintain the services, data models, and APIs that power the platform — designed for correctness, testability, and scale. - Simulation & orchestration: Work on the systems that coordinate complex, multi-step interactions between AI agents and external systems, improving their reliability and throughput. - Evaluation & scoring: Design systems that grade agent outputs, combining deterministic checks with model-assisted judgment — and make scoring reliable, explainable, and reproducible. - Data pipelines: Build pipelines that generate, transform, and quality-check large volumes of structured data and benchmark content. - Quality & reliability: Add the tests, instrumentation, and safeguards needed to trust outputs from systems that are inherently non-deterministic. Qualifications - 4+ years building and shipping production software, with strong proficiency in Python. - Deep software engineering fundamentals: system and API design, data modeling, concurrency/async, testing strategy, debugging, and code review. - Experience designing and operating distributed or service-oriented systems (queues, workers, APIs) — not just calling them. - Comfort designing schemas and working with relational databases, plus the migrations and performance concerns that come with them. - Working knowledge of LLM APIs — orchestration, structured outputs, and handling non-determinism. - Ability to reason about correctness of probabilistic systems: how to test, measure, and trust outputs that aren't byte-for-byte deterministic. - High quality bar: you write tests, types, and docs by default, and you keep changes small and reviewable. Requirements - Bonus points for experience building agentic or multi-agent systems, tool-use, or orchestration frameworks. - Background in evaluation / benchmarking of ML or LLM systems (rubrics, golden datasets, model-as-judge, inter-rater reliability). - Experience with distributed task queues and async workloads. - Modern Python tooling and typed codebases (e.g. type checkers, linters, Pydantic, FastAPI). - Retrieval / search experience and working with data ingest pipelines. - Some comfort with the infra side (Docker, CI/CD) so you can ship what you build. Benefits - Competitive salary. - Training budget to level up your skills from top tech partners like Microsoft, AWS, Salesforce, and Databricks – whether it’s certifications or courses, we’ve got you covered. - Private insurance, top-tier tech gear, and the chance to work with a stellar crew.

Role Description - Design, develop, and deploy AI Agents and GenAI solutions using frameworks such as LangChain, LangGraph, and Azure AI Foundry for enterprise-grade automation workflows. - Build and maintain Retrieval-Augmented Generation (RAG) pipelines integrating vector databases and LLMs for contextual and accurate AI responses. - Fine-tune large language models using parameter-efficient techniques such as LoRA and QLoRA on domain-specific datasets. - Architect and implement scalable backend services using Python, FastAPI, Node.js, and serverless cloud platforms. - Write clean, optimized, and maintainable code with strong adherence to DSA fundamentals and software engineering best practices. - Participate in technical discussions, code reviews, architecture planning, and design reviews to maintain engineering quality standards. - Collaborate with product managers, business analysts, and stakeholders to translate business requirements into technical implementations. - Optimize LLM cost, performance, and latency through prompt engineering, caching strategies, model selection, and inference optimization. - Develop AI-powered automation workflows integrating enterprise tools such as Jira, Confluence, SAP, and CI/CD systems. - Mentor junior engineers and contribute to internal knowledge sharing through documentation, technical sessions, and collaborative discussions. - Participate in sprint planning, stand-ups, sprint reviews, and Agile development processes. - Stay updated with emerging trends in Generative AI, Agentic AI systems, LLM architectures, and cloud-native AI tooling. Qualifications - Strong proficiency in Python and at least one additional programming language, such as JavaScript or TypeScript. - Hands-on experience with GenAI frameworks, including LangChain, LangGraph, Hugging Face, or equivalent technologies. - Strong expertise in Data Structures & Algorithms (DSA), including arrays, strings, linked lists, stacks, queues, trees, graphs, recursion, sorting, searching, hashing, and dynamic programming. - Ability to solve algorithmic and problem-solving challenges with optimized approaches and a clear understanding of time and space complexity using Big-O notation. - Hands-on experience solving coding problems similar to LeetCode medium-level questions as part of technical assessments and interviews. - Strong understanding of selecting optimal solutions and explaining trade-offs between multiple approaches. - Experience building and deploying Retrieval-Augmented Generation (RAG) systems. - Familiarity with vector databases such as Pinecone, ChromaDB, Weaviate, or Cosmos DB. - Strong understanding of REST APIs, backend architecture, and scalable system design principles. - Foundational knowledge of scalable system design concepts including caching, load balancing, distributed systems, concurrency, and performance optimization. - Experience with cloud platforms such as AWS (Lambda, S3, EventBridge) and/or Azure (Functions, AI Foundry, Blob Storage). - Working knowledge of databases, including MongoDB, MySQL, Redis, and PostgreSQL. - Familiarity with Docker, Kubernetes, CI/CD pipelines, and Git workflows. - Understanding of prompt engineering, LLM optimization, embeddings, and AI orchestration workflows. - Knowledge of system scalability concepts including caching, load balancing, distributed systems, and performance optimization. Requirements - Strong analytical and problem-solving capabilities. - Excellent verbal and written communication skills. - Ability to explain complex AI concepts to technical and non-technical stakeholders clearly and confidently. - Strong collaboration and teamwork abilities in distributed and cross-functional environments. - Attention to detail with a quality-focused engineering mindset. - Strong ownership, accountability, and a proactive learning attitude. Preferred Skills - Experience with MCP (Model Context Protocol) and multi-agent orchestration systems. - Exposure to mobile application development using Flutter. - Familiarity with enterprise integrations such as SAP, Salesforce, and RPA tools. - Experience with prompt evaluation and observability frameworks such as LangSmith or Promptfoo. - Exposure to microservices architecture and event-driven systems. - Experience contributing to open-source AI/ML projects or writing technical blogs. - Understanding of AI security, responsible AI practices, and governance principles. Other Relevant Information - Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field. - DSA and algorithmic problem-solving skills will be considered primary evaluation criteria in the first round of interviews. Candidates are expected to demonstrate strong coding proficiency, optimization skills, and clear problem-solving approaches. - 3–5 years of professional software engineering experience with at least 1–2 years focused on GenAI or LLM-based applications. - Experience working on enterprise-scale AI systems and cloud-native applications is highly desirable. - Flexible hybrid/remote working arrangement. - Opportunity to work on cutting-edge GenAI and Agentic AI projects with real-world impact. - Collaborative and learning-oriented engineering culture with regular tech talks and innovation initiatives. - Competitive compensation, health benefits, and professional development support. - Access to the latest AI tools, cloud credits, and research resources. - This role offers the flexibility of working remotely in India.